Cybersecurity threat intelligence and remediation system

ABSTRACT

A cybersecurity system is provided for automated cybersecurity insights, remediation recommendations, and service provisioning. The cybersecurity system can generate threat insights and/or generate remediation recommendations using machine learning models and cybersecurity data obtained from target networks, partners, and the like. To provision cybersecurity services, cybersecurity system may collect metadata regarding the network connections and use cases desired for one or more services. Once the metadata has been collected, the cybersecurity assessment system automatically provisions the selected services based on the provided data, such as duration of time elected, service metrics, and the like.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Application No. 62/862,918, filed Jun. 18, 2019, the contents of which are incorporated by reference herein.

BACKGROUND

Computing devices may communicate with each other over a communication network, such as a local area network (“LAN”), wide area network (“WAN”), the internet, or some combination thereof. For example, a computing device may execute one or more applications that interact with other devices via a network, or that interact with data received from network-accessible data stores. During operation, the computing device may use various communication protocols to send data addressed to various communication ports of destination devices, and receive data via various communication ports.

Communication with other devices over a communication network can expose a computing device to certain risks. For example, vulnerabilities may be introduced by bugs or design flaws in the software and/or hardware that make up a computing device. As another example, vulnerabilities may be introduced by the manner in which a computing device and/or network is configured and used. These and other vulnerabilities can expose a computing device to harmful or malicious activity originating from another device with access to the communication network. Once such vulnerabilities are discovered, various preventive and remedial steps can be taken to mitigate the risks. For example, software fixes (“patches”) can be applied, new hardware can be used, and/or new operating procedures can be implemented.

SUMMARY

The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

One aspect includes systems, methods, and/or non-transitory computer-readable media that provide features for a cloud-based cybersecurity system. The features include obtaining threat data representing a plurality of cybersecurity threats, wherein a first portion of the threat data represents a first cybersecurity threat of the plurality of cybersecurity threats, the first cybersecurity threat detected on a first target network; obtaining vulnerability data representing cybersecurity vulnerabilities associated with individual cybersecurity threats of the plurality of cybersecurity threats, wherein a first portion of the vulnerability data represents a first vulnerability of the first target network at a time associated with the first cybersecurity threat; generating training data using the threat data and the vulnerability data, wherein a first portion of the training data represents the first cybersecurity threat and the first vulnerability; and training a cybersecurity machine learning model using the training data, wherein the cybersecurity machine learning model is configured to generate output data representing a probability of occurrence of one or more cybersecurity threats based on the presence of one or more cybersecurity vulnerabilities.

Another aspect includes systems, methods, and/or non-transitory computer-readable media that provide additional and/or alternative features for a cloud-based cybersecurity system. The features include obtaining threat data representing a plurality of cybersecurity threats, wherein a first portion of the threat data represents a first cybersecurity threat of the plurality of cybersecurity threats, a first network property of a first target network on which the first cybersecurity threat is detected, and a first remediation associated with the first cybersecurity threat; generating remediation data representing a plurality of threat remediations, wherein a first portion of the remediation data represents an association of the first remediation with the first cybersecurity threat and a second cybersecurity threat based at least partly on a similarity between the first portion of the threat data and a second portion of the threat data representing the second cybersecurity threat; generating training data using the threat data and the remediation data, wherein a first portion of the training data represents the first cybersecurity threat, the first network property, and the first remediation; and training a cybersecurity machine learning model using the training data, wherein the cybersecurity machine learning model is configured to generate output data representing a remediation based at least partly on a cybersecurity threat and a network property.

A further aspect includes systems, methods, and/or non-transitory computer-readable media that provide features for a cloud-based cybersecurity system. The features include receiving cybersecurity assessment service setup data representing selection of one or more cybersecurity assessment services to be provisioned for a target network, and configuration information for the target network; generate an instance of a cybersecurity assessment system on one or more computing devices, wherein the instance comprises executable objects and data stores; deploy service-specific executable objects and data stores for each of the one or more cybersecurity assessment services; execute one or more executable configuration objects to configure the one or more cybersecurity assessment services using the configuration information for the target network; provide secure connection information associated with a secure connection to be established between the target network and the instance; establish the secure connection using the secure connection information; and schedule execution of the one or more cybersecurity assessment services.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate example embodiments described herein and are not intended to limit the scope of the disclosure.

FIG. 1 is a block diagram of an illustrative computing environment including a cybersecurity assessment system and a target network according to some embodiments.

FIG. 2 is a user interface diagram of cybersecurity portal showing various interactive tools for assessing cybersecurity status and information regarding a target network according to some embodiments.

FIG. 3 is a user interface diagram showing a real-time cybersecurity status assessment of a target network according to some embodiments.

FIG. 4 is a user interface diagram showing cybersecurity vulnerability information for a target network according to some embodiments.

FIG. 5 is a user interface diagram showing a real-time ticker of cybersecurity events according to some embodiments.

FIG. 6 is a flow diagram of an illustrative process for scanning a target network and obtaining cybersecurity scan data according to some embodiments.

FIG. 7 is a flow diagram of an illustrative process for analyzing cybersecurity scan data and assessment data to determine a real-time cybersecurity status according to some embodiments.

FIG. 8 is a flow diagram of an illustrative process for determining an overall cybersecurity score according to some embodiments.

FIG. 9 is a flow diagram of an illustrative process for transforming a vulnerability scan data file into mapped format files for interactive presentation according to some embodiments.

FIG. 10 is a block diagram of an illustrative cybersecurity vulnerability scan file and corresponding mapped format files according to some embodiments.

FIG. 11 is a block diagram of an illustrative computing environment including a cybersecurity assessment system and multiple target networks according to some embodiments.

FIG. 12 is a block diagram of illustrative hierarchies of target networks and authorization relationships between target networks according to some embodiments.

FIG. 13 is a flow diagram of an illustrative process for generating a user interface according to authorization configuration in a hierarchical cybersecurity status environment according to some embodiments.

FIG. 14 is a user interface diagram showing hierarchical cybersecurity status information for a group of target networks according to some embodiments.

FIG. 15 is a flow diagram of an illustrative process for configuring and maintaining a hierarchical group of target networks according to some embodiments.

FIG. 16 is a block diagram showing example interactions between a target network and multiple hierarchical cybersecurity instances according to some embodiments.

FIGS. 17A, 17B, and 17C are block diagrams of illustrative computing environments and components thereof, including a cybersecurity system and partner(s), according to some embodiments.

FIGS. 18A, 18B, and 18C are block diagrams of illustrative cybersecurity machine learning model training according to some embodiments.

FIG. 19A is a flow diagram of an illustrative process for automated threat analysis according to some embodiments.

FIG. 19B is a flow diagram of an illustrative process for automated remediation according to some embodiments.

FIG. 20 is a flow diagram of an illustrative process for provisioning of cybersecurity assessment services according to some embodiments.

FIG. 21 is a user interface diagram showing controls for selecting and configuring cybersecurity assessment services according to some embodiments.

FIG. 22 is a user interface diagram showing a task list and service provisioning status according to some embodiments.

FIG. 23 is a block diagram showing example interactions between a target network, a provisioning management unit, and a cybersecurity instance during provisioning of the cybersecurity instance according to some embodiments.

FIG. 24 is a block diagram showing additional example interactions between a target network, a provisioning management unit, and a cybersecurity instance during provisioning of the cybersecurity instance according to some embodiments.

FIG. 25 is a block diagram of illustrative computing systems configured to implement features of the present disclosure according to some embodiments.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The present disclosure is directed to a cybersecurity assessment system for monitoring, assessing, and addressing the cybersecurity status of a target network and/or a hierarchical group of target networks. The cybersecurity assessment system may be a cloud-based system that accesses and scans target networks remotely. The scan may produce data regarding the current state and properties of devices on a target network, events occurring on the target network, vulnerabilities detected in devices on the target network, and the like. The cybersecurity assessment system can analyze the scan data and determine a degree to which the current status of the target network satisfies a particular cybersecurity assessment framework, and how the status changes over time. The cybersecurity assessment system can also transform large amounts of vulnerability scan data into efficient representations for use in providing interactive presentations of the vulnerabilities detected on the target network. The cybersecurity assessment system can also provide information regarding cybersecurity events in substantially real time.

Conventional systems for monitoring cybersecurity status are associated with a number of costs, inefficiencies, and undesirable effects. For example, conventional systems require the installation of software and/or hardware on the target network to scan the devices connected to the network. As another example, the output of a cybersecurity scan may be a document—which may be hundreds or thousands of pages in length—that provides a narrative of the scan data in a difficult-to-consume format designed to generate additional consultative work to interpret and address the scan data. As a further example, the scan tools require on-site administration to manage, run, and interpret the resulting data for actionable insights into the cybersecurity status of the target network. As yet another example, cybersecurity event alerting and remediation recommendations may not be integrated into the overall cybersecurity package and do not provide adequate information. These and other shortcomings of conventional systems make it difficult—if not impossible from a practical standpoint—for enterprises to efficiently and effectively monitor, assess, and address the cybersecurity status of their networks in substantially real-time. In addition, groups of entities (e.g., companies), each operating their own network, may not be able to share cybersecurity status information with acceptable access control measures that reflect the hierarchical relationships among the entities.

Aspects of the present disclosure address, among other things, issues with cybersecurity assessment such as those discussed above. More specifically, a cloud-based cybersecurity assessment system is disclosed. The cybersecurity assessment system obtains, from multiple disparate sources, data regarding the cybersecurity status of a target network. The data may be obtained remotely, without necessarily requiring installation of any hardware or software at the target network site. For example, the data may include scan data regarding the current state of devices on the network, cybersecurity events occurring on the network, current vulnerabilities on the network, and the like. In some embodiments, the cybersecurity assessment system analyzes the scan data and determines the degree to which the current status of the target network satisfies the requirements of one or more cybersecurity assessment frameworks. The target network is initially assessed against individual factors (also referred to as “controls”) of a framework to determine whether the target network satisfies the requirements of the framework. The scan data is analyzed to determine adjustments to initial assessment. For example, scan data can be used to “prove” whether an initial assessment of a particular control is indeed correct and shown in the scan data. As another example, scan data can be used to adjust the degree to which the target network satisfies individual controls at different levels between a binary yes/no initial assessment. Once the assessments of individual controls have been adjusted based on the objective scan data, an overall score can be determined for the target network indicating the degree to which target network has been shown—through objective scan data—to satisfy the requirements of the framework being used.

Additional aspects of the present disclosure relate to processing vulnerability scan data and presenting an interactive interface through which the vulnerability scan data can be accessed. The scan data generated by a vulnerability scan may be substantial (e.g., hundreds of gigabytes up to terabytes or more). Presenting such a large amount of data in an intelligent manner to allow users to get actionable insights may be difficult or impossible without first processing the data into an efficient form. In some embodiments, the cybersecurity assessment system can process the vulnerability scan data to identify duplicate instances of data items, such as references to specific vulnerabilities, remediation recommendations, network devices, etc. The system can remove duplicate instances and replace them with a single representative instance. Any number of unique data items can then be mapped to the representative instance instead of being associated with separate duplicate instances. By repeating this process for different types and groups of data items in the vulnerability scan data file, the initially large file can be dramatically reduced in size without any loss of data that is used when generating a dynamic user interface for viewing the vulnerabilities identified by the vulnerability scan. Illustratively, the vulnerability user interface may be an interactive display that summarizes the vulnerabilities detected across the network, provides detailed information regarding individual vulnerabilities, and allows presentation at various degrees of granularity between these extremes. For example, the vulnerability user interface may include color-coded severity indicators and display objects that represent groups of vulnerabilities (e.g., groups of devices that each exhibit a particular vulnerability or set of vulnerabilities). A user may activate an individual display object to obtain more information about the group of devices/vulnerabilities that the display object represents.

Further aspects of the present disclosure relate to managing cybersecurity information for hierarchical groups of target networks. Entities with target networks may be related to each other in various manners, such as parent-child, siblings, and the like. The entities may desire to share cybersecurity information with other related entities. For example, an entity (e.g., a parent company) may have any number of subsidiaries, and any or all of the subsidiaries may operate their own target network that is separate from that of the parent company and separate from that of other subsidiaries of the parent company. In addition, the subsidiaries may each have their own subsidiaries, and so on. As another example, an entity may be part of a group of distinct entities, such as a supply chain, that includes any number of entities contributing to the overall production of goods and/or services. The supply chain may be organized in a hierarchical manner with any number of children and/or siblings associated with a particular entity. In these and in other examples, entities may desire to enforce cybersecurity policies and share cybersecurity information to various degrees among the entities in the hierarchy. To facilitate both the sharing of, and secure access to, the cybersecurity information of various entities in a hierarchy, certain configurations may be used. In some embodiments, a separate instance of the cybersecurity assessment system may be created for a particular hierarchy under the control of a particular entity. For example, an entity may be the parent or owner of the hierarchy, such as when the entity is a parent company with multiple subsidiaries, or when the entity is the customer to which the goods and services of a supply chain are directed. The instance of the cybersecurity assessment system may be separated from all other instances such that the data objects, configuration information, and executable code for the instance are stored in physically separate files—and potentially on physically separate devices—than the data objects, configuration information, and executable code for the other instances. This physical segregation can help to ensure that there is no unauthorized access to cybersecurity information outside of the hierarchy. If a particular entity is part of multiple hierarchies with separate instances, some or all of the cybersecurity information for that entity may be copied to the physical files of the various instances, rather than stored in one location with access provided to other entities in other instances. Such duplication and physical separation can further prevent unauthorized access to the cybersecurity information of other entities in other instances. Within an instance, access to the cybersecurity information of the entities in the hierarchy may be enforced according to standardized or customized configuration policies.

Still further aspects of the present disclosure relate to generating cybersecurity information for a hierarchical group of entities in a single instance of the cybersecurity assessment system. For a particular hierarchy, overall cybersecurity metrics can be generated based on metrics for each of the individual entities. For example, a cybersecurity score for the hierarchy may be generated based on cybersecurity scores of each individual entity of the hierarchy. As another example, a summary of detected vulnerabilities may be generated from the vulnerabilities detected on each individual target network of the hierarchy. Depending upon the particular configuration policy for the hierarchical group and current entity, a user may be permitted to “drill down” or “drill up,” as desired, to obtain more detailed information regarding entities in the hierarchy.

Various aspects of the disclosure will now be described with regard to certain examples and embodiments, which are intended to illustrate but not limit the disclosure. Although aspects of some embodiments described in the disclosure will focus, for the purpose of illustration, on particular examples of networks, devices, vulnerabilities, events, assessment frameworks, and algorithms, the examples are illustrative only and are not intended to be limiting. In some embodiments, the techniques described herein may be applied to additional or alternative networks, devices, vulnerabilities, events, assessment frameworks, and algorithms. In addition, any feature, process, device, or component of any embodiment described and/or illustrated in this specification can be used by itself, or with or instead of any other feature, process, device, or component of any other embodiment described and/or illustrated in this specification.

Network-Based Cybersecurity Assessment Environment

With reference to an illustrative embodiment, FIG. 1 shows an example network environment in which aspects of the present disclosure may be implemented. As shown, the network environment may include a target network environment 100 and a cybersecurity assessment system 120. The cybersecurity assessment system 120 and devices of the target network environment 100 may communicate with each via one or more communication networks 115. In some embodiments, a communication network 115 (also referred to simply as a “network”) may be a publicly-accessible network of linked networks, possibly operated by various distinct parties, such as the internet.

Generally described, a target network environment 100 (also referred to as a “target network” for convenience) may be a network environment operated by an entity (e.g., an enterprise, user, or some other client). Target network 100 may comprise a plurality of interconnected devices that may communicate with one another via one or more communication networks, which may include or be separate from network 115. For example, target network 100 may include network infrastructure 110, which may be used to implement a personal area network (“PAN”), local area network (“LAN”), wide area network (“WAN”), global area network (“GAN”), or some combination thereof, any or all of which may or may not have access to and/or from the internet. In some embodiments, target network 100 may be an on-premises network environment which the network infrastructure and associated services are primarily provided on an entity's premises. In some embodiments, target network 100 may be a cloud-based network in which all or a significant portion of the network infrastructure and services are remotely provided by a network service provider separate from the entity. In some embodiments, target network 100 may be a hybrid in which some components and services are provided on an entity's premises, and some components and services are provided remotely by a network service provider.

In some embodiments, as shown in FIG. 1 , a target network 100 includes various devices, such as one or more mobile devices 102, desktop devices 104, servers 106, audiovisual devices 108, other types of devices, or any combination thereof. Illustratively, mobile devices 102 may include mobile telephones with program execution and network communication capabilities (e.g. “smart phones”), wearable devices with program execution and network communication capabilities (e.g., “smart watches,” “smart eyewear”), tablet computing devices, electronic reader devices, handheld video game devices, media players, notebook computers, and the like. Desktop devices 104 may include personal computing devices, terminal computing devices, and the like. Servers 106 may include “blade” servers, midrange computing devices, mainframe computing devices, and the like. Audiovisual devices 108 may include televisions with program execution and network communication capabilities (e.g., “smart TVs”), television set-top boxes, video game consoles, video cameras, still image cameras, microphones, speakers with program execution and network communication capabilities (e.g., “smart speakers”), and the like.

While the example of FIG. 1 displays a limited set of example mobile devices 102, desktop devices 104, servers 106, and audiovisual devices 108, it will be appreciated that other arrangements may exist in other embodiments. For example, a target network 100 associated with a large enterprise may comprise hundreds or thousands of personal computing devices, servers, cameras, televisions, wireless routers, telephones, and/or other network-connected devices. In some embodiments, other types of devices altogether may be part of a target network 100. For example, a target network may include network-enabled printers, copiers, scanners, fax machines, medical devices, appliances, lights, vehicles, or any other device with network communication capabilities.

Cybersecurity assessment system 120 may be configured to evaluate the cybersecurity posture of the network environment represented by target network 100 by connecting to the target network 100 via network 115. For example, the cybersecurity assessment system 120 may connect to the target network 100 through a virtual private network (“VPN”) connection over the network 115. Communication with the target network 100 may therefore occur using such a VPN tunnel. In this way, the cybersecurity assessment system 120 can be provided with secure access to the target network 100 even though the cybersecurity assessment system 120 is remote from the target network 100, and even though communications to/from the target network 100 occur over the network infrastructure of network 115. A single cybersecurity assessment system 120 may be configured to assess the cybersecurity status of any number of target networks 100. In some embodiments, a single target network 100 may be assessed by multiple cybersecurity assessment systems 120.

Cybersecurity assessment system 120 may comprise a plurality of components. In some embodiments, cybersecurity assessment system 120 comprises one or more data stream units 125 (e.g., data stream unit 125A, data stream unit 125B, data stream unit 125C), aggregation unit 130, cybersecurity unit 140, transform unit 150, user interface unit 160, and data store 180. Individual components of the cybersecurity assessment system 120 may be implemented one or more computing devices. For example, each component may be implemented on a separate computing device, or separate set of computing devices. As another example, a single computing device or set of computing devices may be shared among multiple components. In some embodiments, the features and services provided by the cybersecurity assessment system 120 may be provided by one or more virtual machines implemented in a hosted computing environment. The hosted computing environment may include one or more rapidly provisioned and released computing resources, such as computing devices, networking devices, and/or storage devices. A hosted computing environment may also be referred to as a “cloud” computing environment.

As discussed in greater detail below, cybersecurity assessment system 120 may monitor the status of target network 100 by processing one or more streams of data regarding the target network 100. For example, data stream unit 125A may be configured to generate a first data stream by continuously monitoring the current hardware and/or software configuration status of devices in target network 100. This data stream may be referred to as continuous cybersecurity monitoring (“CCM”) data, or simply as “continuous monitoring data” for convenience. As another example, data stream unit 125B may be configured to generate a second data stream corresponding to system logs, event logs, error logs, and/or other event data collected from devices of the target network 100. This data stream may be referred to as a secure information and event management (“SIEM”) data, or simply as “event data” for convenience. As a further example, data stream unit 125C may be configured to generate cybersecurity vulnerability data identifying vulnerabilities in hardware and software components in a network environment, such as missing updates, unsecured ports, deprecated technology, and the like. This data stream may be referred to as “vulnerability data.” Collectively, the data generated by the data stream units may be referred to as raw cybersecurity scan data, or simply as “raw scan data” for convenience.

In some embodiments, aggregation unit 130 may receive the raw scan data generated by data stream units 125A, 125B, and 125C and store the data in a format that may be more easily utilized by other components of the cybersecurity assessment system 120. The aggregation unit 130 may do so by first converting the raw scan data into a specified file format. The aggregation unit 130 may access the raw scan data generated by the plurality of data streams in one or more databases, files, or other data storage structures. In some embodiments, the raw scan data may be stored by the data stream units 125 in a database or distributive file system (e.g., a file system utilizing Hadoop Distributed File System or “HDFS” architecture) for fast access to large data sets, in a relational database (e.g., a Structured Query Language or “SQL” database), or the like. The aggregation unit 130 may format the raw scan data into a format usable by downstream components, such as the cybersecurity unit 140, transform unit 150, etc. Illustratively, the aggregation unit 130 may process the data into a structured format such as one or more JavaScript Object Notation (“JSON”) files, eXtensible Markup Language (“XML”) files, or the like. In some embodiments, the processed data may be stored in data store 180. Collectively, the processed data may be referred to as cybersecurity scan data, or simply as “scan data” for convenience.

The scan data compiled and stored by the aggregation unit 130 may be accessed by cybersecurity unit 140, transform unit 150, user interface unit 160, and/or other components of the cybersecurity assessment system 120 to provide various features, as discussed in greater detail below. In some embodiments, cybersecurity unit 140 may utilize an algorithm to assess the current cybersecurity posture or “status” of the target network 100 based at least partly on the scan data. An example algorithm for assessing the current cybersecurity status of the target network 100 with respect to a cybersecurity assessment framework is discussed in greater detail below with respect to FIGS. 7 and 8 . The result of the cybersecurity status assessment can be presented using one or more user interfaces, such as the interfaces shown in FIGS. 2 and 3 . In some embodiments, vulnerability scan data can be processed by the transform unit 150 to generate an efficient data structure representing all vulnerabilities discovered on the target network 100. An example algorithm for transforming vulnerability scan data is described in greater detail below with respect to FIGS. 9 and 10 . The resulting data structure can be used to present vulnerability data using one or more user interfaces, such as the interfaces shown in FIGS. 2 and 4 . In some embodiments, event data can be presented via one or more user interfaces, such as the interfaces shown in FIGS. 2 and 6 .

User Interfaces

FIG. 2 is a user interface diagram of a cybersecurity portal interface 200 showing various interactive tools for assessing cybersecurity status and information regarding a target network 100. Illustratively, portal interface 200 may provide an easy-to-understand display of high-level cybersecurity information regarding the target network 100 on a single page or screen. Users may also access more detailed cybersecurity information through the portal interface 200 if desired.

Portal interface 200 may comprise various options 210, 214, 218, and 222 that a user may select to view specific presentations associated with the cybersecurity assessment system 120. In the example of FIG. 2 , option 210 is titled “Documents” and may be associated with a documentation repository. In some embodiments, the user may access option 210 to view reports, vulnerability scans, or cybersecurity posture assessments of the target network 100. Option 214 is titled “Continuous Cybersecurity Monitoring” and may be associated with reports, controls, and configuration settings for managing the aspects of the cybersecurity assessment system. Option 218 is titled “Cyber Status.” Selection of option 218 may cause presentation of an interface, such as cybersecurity status interface 300 discussed in greater detail below, that uses information generated through a cybersecurity status assessment process to visually represent the cybersecurity status of the target network 100 with respect to one or more cybersecurity assessment frameworks. In some embodiments, option 218 may be a dynamically-generated display that presents a summary of the current or most-recently-determined cybersecurity status of the target network 100 directly on portal interface 200 without necessarily requiring selection or other user interaction. Option 222 is titled “Vulnerabilities.” Selection of option 222 may cause presentation of an interface, such as vulnerabilities interface 400, that utilizes vulnerability data to identify and present vulnerabilities detected on the target network 100, to present remediation recommendations to address the vulnerabilities, to provide export of vulnerability data to other systems, etc. In some embodiments, option 222 may include a dynamically-generated display that presents a summary of the current or most-recently-identified vulnerabilities of the target network 100 on portal interface 200 without necessarily requiring selection or other user interaction. For example, option 222 may include an instance of the vulnerabilities tracker 418 shown in FIG. 4 and discussed in greater detail below. Different visual characteristics of portions of displayed tracker may be dynamically determined to representing different dimensions of the vulnerability data being represented. Illustratively, color may be varied to represent severity, length may be varied to represent a count of vulnerabilities, width may be varied to represent a count of affected devices, other visual characteristics may be varied, other dimensions of vulnerability data may be used, and/or other combinations may be implemented.

In some embodiments, portal interface 200 may also include cybersecurity event ticker 224. Cybersecurity event ticker 224 can present real-time alerts, warnings, and notifications of cybersecurity events and risks detected during continuous cybersecurity monitoring, event scanning, and vulnerability scanning. In some embodiments, ticker 224 displays cybersecurity event display objects 226A, 226B, 226C, 226D, and 226E in the order in which each event is detected by the cybersecurity assessment system 120. In some embodiments, selection of a cybersecurity event object—or the ticker 224 in general—may cause display of ticker interface 500, discussed in greater detail below. While the ticker 224 is portrayed as a component of portal interface 200, it will be appreciated that in some embodiments the alerts and warnings presented in ticker 224 may be transmitted to a client system on the target network 100, stored in a database within the cybersecurity assessment system 120, or transmitted to a remote device.

FIG. 3 is a user interface diagram showing a real-time cybersecurity status interface 300. In some embodiments, as shown, the cybersecurity status interface 300 comprises a dynamic cybersecurity status indicator 302, description boxes 308, 316, and 320, and cybersecurity assessment framework compliance chart 312. Cybersecurity status interface 300 may be used for presenting the current status of the target network 100 with respect to a particular framework in substantially real-time. In generating cybersecurity status interface 300, the user interface unit 160 or some other component of the cybersecurity assessment system 120 may analyze past and present assessments and remediation completions, and generate displays of the cybersecurity status. In some embodiments, the cybersecurity status interface 300 may incorporate data from previous and current cybersecurity reports, and present a dynamic visualization of the change in cybersecurity status over time.

Status indicator 302 presents a snapshot summary of overall cybersecurity status after the cybersecurity assessment system 120 has analyzed the current state of target network 100 with respect to a cybersecurity assessment framework. Status indicator 302 comprises sections 304A, 304B, and 304C, wherein section 304A visually represents a portion of all analyzed cybersecurity controls that target network 100 fails to fully satisfy, section 304B visually represents the portion of the analyzed cybersecurity controls that the target network 100 satisfies, and section 304C represents an overall assessment score (also referred to as the cybersecurity status score) for the target network 100. Box 308 presents a cybersecurity assessment score for the target network 100, which in this case is the total percentage of compliance with all requirements of a cybersecurity framework to date. Box 316 presents statistics pertaining to the proportion of satisfied cybersecurity framework requirements compared to the amount of such requirements that are deficient in the target network 100. The cybersecurity assessment system 120 may be able to compile these statistics by means of user-provided input or automatic detection based on the received data streams via data stream unit 125A, 125B, and/or 125C. In some embodiments, cybersecurity assessment framework controls and families of controls are analyzed and mapped to chart 312, wherein each family is associated with a level of completion based at least partly on the proportion of completed or satisfied controls in the family. In some embodiments, cybersecurity status interface 300 may display box 320 to represent specific statistics pertaining to the level of completion of cybersecurity assessment framework families listed in chart 312. While FIG. 3 illustrates a snapshot of the target network's cybersecurity posture, it will be appreciated that the interface 300 may change dynamically to reflect changes to the user system in real-time.

FIG. 4 is a user interface diagram of a vulnerabilities interface 400 showing cybersecurity vulnerability information for a target network 100. The vulnerabilities interface 400 comprises a chart 410 displaying historical statistics regarding the amount of vulnerabilities over a given time period. In some embodiments, chart 410 may categorize vulnerabilities according to category, such as a severity level. For example, the vulnerability record dated Jan. 1, 2018 in chart 410 comprises a total of 600 vulnerabilities that are divided into three top-level categories corresponding to three severity levels. Illustratively, chart 410 may be a bar chart with individual bars divided into sections 414A, 414B, and 414C, wherein section 414A represents the vulnerabilities associated with a critical severity level, section 414B represents vulnerabilities associated with a high severity level, and section 414C represent vulnerabilities associated with a medium severity level. In some embodiments, vulnerabilities may be associated with a particular severity level based on classifications presented in existing cybersecurity frameworks or by supervised training in a machine learning model using trained data sets associating particular vulnerabilities with a particular severity level. In some embodiments, each section 414A, 414B, and 414C may be selectable via a user input. Selecting a section 414A, 414B, or 414C may allow a user to further view details regarding the vulnerabilities of the selected severity level in dynamic vulnerabilities tracker 418.

In FIG. 4 a user has selected, using a selection action (e.g., a mouse click, touch gesture, keyboard shortcut, voice command, etc.), section 414B. Vulnerabilities interface 400 may then update display of dynamic vulnerabilities tracker 418 to provide a visual representation of collected statistics regarding the vulnerabilities determined to be associated with a high severity level (e.g., according to a cybersecurity framework or by the cybersecurity assessment system's own learned classification). In some embodiments, vulnerabilities tracker 418 may be a dynamic pie chart configured to visually represent multiple dimensions of information simultaneously by dynamically modifying visual characteristics of different sections of the vulnerabilities tracker 418 to represent the different dimensions of vulnerability information. For example, dynamic vulnerabilities tracker 418 may be configured to display an overall number of vulnerabilities associated with section 414B along with a relevant date and severity level associated with the vulnerability data of 414B. Furthermore, in some embodiments dynamic vulnerabilities tracker 418 may display a plurality of sections 420A, 420B, 420C, and 420D. Certain visual characteristics (e.g., length, width, color, visual texture) of each of sections 420A, 420B, 420C, and 420D may be customized to indicate how many detected vulnerabilities are determined to affect a quantity of different devices. For example, the length of section 420A (as measured in a circumferential direction) may represent 77 vulnerabilities, while the width of section 420A (as measured in a radial direction) may indicate that a set of 10 different devices in target network 100 are each affected by the same set of 77 vulnerabilities. Similarly, visual characteristics of section 420B may be customized to indicate that a set of 152 vulnerabilities affect each of a set of 5 different devices in target network 100, visual characteristics of section 420C may be customized to indicate that a set of 9 vulnerabilities affect each of a set of 3 different devices, and visual characteristics of section 420D may be customized to indicate that a set of 8 vulnerabilities affect each of a set of 8 different devices in the target network 100.

In some embodiments, each section 420A, 420B, 420C, and 420D may be associated with detailed information that may be accessed by a user through a user selection action (e.g., mouse click or a touch gesture). For example, a central portion of dynamic vulnerabilities tracker 418 may show a name or brief description of the vulnerabilities represented by the selected section (e.g., section 420C in this case). Vulnerabilities interface 400 may also update display window 422 to present detailed information in response to a user click on a section 420C. Window 422 may display date information, the severity level of the selection, a name of a vulnerability in section 420C, an identifier (e.g., a common vulnerabilities and exposures or “CVE” number) associated with the vulnerability, a synopsis of the vulnerability, a brief description of the vulnerability, and a proposed solution to address or remedy the vulnerability. The detailed information displayed in window 422 may, in some embodiments, be determined by cybersecurity assessment system 120 by referring to cybersecurity frameworks and vulnerability scans of the target network 100. For example, a standardized list of specific vulnerability identification numbers and corresponding vulnerabilities may provide a brief textual description of the vulnerability in question. In some embodiments, the information displayed in window 422 may also comprise learned information from a machine learning model (e.g., whether to classify a particular vulnerability associated with a vulnerability identification number as a high severity level). Although only one vulnerability associated with one host is displayed in window 422, it will be appreciated that the window 422 may display additional entries according to some embodiments. In some embodiments, data regarding the vulnerabilities displayed in window 422 may be exported to another system, to a file, etc. For example, a user may select export option 412 to initiate export of the vulnerability data.

FIG. 5 is a user interface diagram showing a real-time cybersecurity event ticker interface 500 for displaying cybersecurity event data. Ticker interface 500 may comprise ticker 505, event chart 510, and control elements 520A, 520B, and 520C. In some embodiments, ticker interface 500 may be accessed via portal 200. Ticker interface 500 may generate alerts and notifications based on analytics of data regarding cybersecurity events detected on the target network, including events dealing with devices, ports, vulnerabilities, software, users, and threat intelligence in correlation with the target network's cybersecurity monitoring. The data from which the events are detected may come from various scans of the target network 100, such as those discussed above and in greater detail below. Illustratively, this data may include event data, cybersecurity monitoring data, vulnerability data, other data, or some combination thereof. The cybersecurity assessment system 120 can analyze the data based on various detection rules to identify which events are to be added to the ticker 505. The detection rules may specify particular characteristics of events that are to be added to the ticker 505, such as events with a particular level of severity (e.g., critical events specified as such in the scan data itself) and/or events that satisfy certain use cases (e.g., critical events that are detected based on data from multiple sources, which a single source may not identify as critical). For example, an unsuccessful login event followed by a successful login event on a weekday morning at 9:00 AM may both be recorded in an event log, and the records (or data derived therefrom) may be included in event data. When the cybersecurity assessment system 120 analyzes the event data, it may determine not to include either of these events in the ticker 505 because they do not satisfy any detection rules (e.g., the unsuccessful and successful logins are within expected parameters) and/or the events is not flagged as critical during event scanning. However, a successful login event on a weekend at 2:00 AM (with or without a prior unsuccessful login event), or three consecutive unsuccessful events at any time (with or without a subsequent successful login event) may satisfy the conditions of a use case to be included in the ticker 505 (or to be the subject of some other type of alert).

Ticker 505 may display recent alerts that are responsive to events in real-time and represent all detected use cases or cybersecurity events. In some embodiments, ticker 505 may dynamically update to include warnings or alerts from cybersecurity events that occur in real-time. Alerts and warnings displayed in the ticker 505 may be selected via user input to display detailed information regarding the selected alert or warning. For example, ticker entries 530, 540, and 550 each display warning messages received on Jan. 1, 2018 in chronological order, corresponding to the items in ticker 505 (and, in some embodiments, corresponding to items in ticker 244 of the portal interface 200). Each entry 530, 540, and 550 may contain detailed information, such as the name of the warning or alert, the MAC address of the affected device, the priority type of the entry, an IP address associated with the affected device, and a time of the alert or warning.

Event chart 510 visually displays historical data regarding alerts and warnings detected by the cybersecurity assessment system 120. In some embodiments, event chart 510 may illustrate a change in the amount of alerts the system receives over a set period in time (e.g., over the course of a week, month, or year). In some embodiments, control elements 520A, 520B, and 520C may be used to filter the results shown in the ticker entries 530, 540, and 550. For example, if a user clicks on button 520C, then the ticker interface 500 may only display cybersecurity events associated with a “WARNING” Priority category.

Cybersecurity Scanning

FIG. 6 is a flow diagram of an illustrative process 600 for scanning a target network 100 and obtaining cybersecurity scan data. Process 600 begins at block 605. Illustratively, process 600 may begin automatically in response to an event or on a predetermined or dynamically-determined schedule. In some embodiments, process 600 may be initiated by a system administrator, client, or the like.

At block 610, the cybersecurity assessment system 120 may connect one or more scanning components to a target network 100 to obtain raw cybersecurity scan data. In some embodiments, data stream units 125A, 125B, 125C, other data stream units, or some combination thereof may be configured to connect to target network 100 via network 115 (e.g., using a VPN connection through network 115).

At block 615, data stream unit 125A may generate continuous monitoring data by performing device discovery and profiling on all devices that have joined target network 100. For example, data stream unit 125A may obtain information about any device with an internet protocol (“IP”) address on the target network 100, and add the information (or information derived therefrom) to the continuous monitoring data. The information for a given device may include: hardware characteristics (e.g., device type, device vendor, installed hardware components); software characteristics (e.g., operating system type and version, installed application components, etc.); metadata regarding the device's connection to the target network (e.g., IP address, media access control or “MAC” address, secure communication certificates such as Secure Sockets Layer or “SSL” certificates, etc.); other information; or any combination thereof. The continuous monitoring data may come in the form of data representing a complete scan in which every device connected to the target network 100—or some subset thereof—is scanned, continuous monitoring data is generated, and then the process of scanning every device on the network begins again. In some embodiments, discrete sets or items continuous monitoring data may be generated for subsets of devices, device-by-device, or characteristic-by-characteristic, as the data stream unit 125A scans the target network 100.

At block 620, data stream unit 125B may generate event data by obtaining event logs from devices on the target network 100. For example, data stream unit 125B may obtain event logs, security logs, error logs, and the like. The log data for a given device may include: records of individual logins; records of individual logouts; records of individual unsuccessful logins; records of individual web site visits; records of applications executed; records of individual file creation, access, modification, and delete events; records of warnings and/or errors; other information; or any combination thereof. Records for individual events may include timestamps representing the time of the event, unique device identifiers (e.g., IP address, MAC address), unique event identifiers, event descriptions, categories (e.g., network event, application event, error), severity levels (e.g., critical, high, medium, low), etc. The event data may come in the form of data representing a complete scan in which logs on every device connected to the target network 100—or some subset thereof—are scanned and the content of the logs (or data derived therefrom) is added to the event data. In some embodiments, when log files on a device are updated, the updated log information may be provided to the data stream unit 125B.

In some embodiments, the data stream unit 125B may be configured to analyze log files and/or other information to detect the occurrence of particular events satisfying particular criteria, separate from the events that are recorded in the logs by the devices themselves. Such detected events and associated criteria may be referred to as “use cases.” For example, a use case for a particular target network 100 may involve detecting occurrence of a “high risk” login event when a user account is used to log into a device more than a threshold period of time before or after such a log in event is expected (e.g., at 2:00 AM, when logins for this user account or device typically occur at 9:00 AM+/−1 hour). When event data are analyzed and criteria for a use case are satisfied, event data for a custom use case event can be generated. A given target network 100 may use any number of use cases, and different target networks 100 may use the same or different use cases.

At block 625, data stream unit 125C may generate vulnerability data by scanning devices on the target network 100 to detect cybersecurity vulnerabilities. Generally described, vulnerabilities include any state, configuration, or characteristic of a device that puts the device at risk from a cybersecurity standpoint. For example, vulnerabilities may include: missing or out of date security components; missing or out of date corrective measures (“patches”); communication ports being open when they should not be open; use of unsupported or otherwise deprecated technology; etc. Data stream unit 125C may communicate with devices on the network to: obtain metadata indicating the current version or state of software and/or hardware components of the device; detect which communication ports are open; detect the signature of various other vulnerabilities; etc. Illustratively, the vulnerabilities that are detected by the data stream unit 125C may be defined in a standardized list of known vulnerabilities (e.g., the list of common vulnerabilities and exposures, also known as “CVEs”). Data representing the vulnerabilities may include unique device identifiers, unique vulnerability identifiers (e.g., ID numbers from a list of known vulnerabilities), vulnerability descriptions, categories, severity levels, etc.

At block 630, the cybersecurity assessment system 120 may aggregate the data from the first, second, and third data streams. In some embodiments, the raw scan data may be stored by the data stream units in a database or distributive file system (e.g., a file system utilizing Hadoop HDSF architecture) for fast access to large data sets, in a SQL database, or the like. The aggregation unit 130 may format the raw scan data into a format usable by downstream components, such as the cybersecurity unit 140, transform unit 150, etc. Illustratively, the aggregation unit 130 may process the data into a structured format such as one or more JSON files, XML files, etc. The formatted data may be stored in data store 180.

At block 635, process 600 may end. Although the blocks of FIG. 6 are shown and described as occurring sequentially, in some embodiments certain blocks may be performed in a different order, in parallel, asynchronously, repetitively, etc. For example, blocks 615, 620, and 625 may be performed concurrently after block 610. As another example, blocks 615, 620, 625, and/or 630 and may be repeated continuously until a stopping event occurs (e.g., a system administrator ends process 600).

Cybersecurity Status Processing

FIG. 7 is a flow diagram of an illustrative process 700 for analyzing cybersecurity scan data and assessment data to determine a real-time cybersecurity status. Process 700 begins at block 705. Process 700 may begin in response to an event, such as when scan data is generated by the aggregation unit 130 or upon receiving a command from a system administrator. In some embodiments, process 700 may be executed according to a predetermined or dynamically determined schedule. When process 700 is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable (e.g., random access memory or “RAM”) of a computing device. For example, cybersecurity analysis instructions 2574 shown in FIG. 25 may be loaded into memory 2566 of a cybersecurity assessment system computing device 2550 and executed by one or more processors 2560. In some embodiments, process 700 or portions thereof may be implemented on multiple processors (on the same or separate computing devices), serially or in parallel.

At block 710, cybersecurity unit 140 or some other component of the cybersecurity assessment system 120, may obtain scan data (e.g., as processed and stored in data store 180 during process 600).

At block 715, the cybersecurity unit 140 or some other component of the cybersecurity assessment system 120 may identify a cybersecurity assessment framework against which target network 100 is to be evaluated to determine the current cybersecurity status.

Generally described, a cybersecurity assessment framework is associated with a particular cybersecurity goal or set of goals, such as securing sensitive data, ensuring the integrity of transactions, preventing unauthorized operations, and the like. To help determine whether a given target network achieves the desired goal(s), a cybersecurity assessment framework may include any number of cybersecurity factors against which a target network is assessed for compliance. A cybersecurity factor is associated with a property or feature that is desirable to achieve the goal(s) of the cybersecurity assessment framework. Illustratively, a target network may be assessed to determine whether the property or feature is present and the cybersecurity factor is therefore satisfied. The assessment may be a binary assessment (either the cybersecurity factor is or is not satisfied) or it may be an assessment by matter of degree (the cybersecurity factor may be partially satisfied to varying degrees between total satisfaction and total failure). A total score for the cybersecurity assessment framework may be generated based on scores determined for individual cybersecurity factors. The total score represents the degree to which the target network is in compliance with the applicable cybersecurity assessment framework, and thus the total score may represent the current cybersecurity status of the target network.

A cybersecurity assessment framework, also referred to as a cybersecurity framework for convenience, may include tens, hundreds, or more individual cybersecurity factors. In some embodiments, the cybersecurity factors may be grouped into subsets of similar or related cybersecurity factors. Cybersecurity factors may also be referred to as “controls,” and subsets of similar or related cybersecurity factors may also be referred to as “families.” In one specific non-limiting embodiment, a cybersecurity framework may be based at least partly on a standardized set of requirements, such as National Institute of Standards and Technology (“NIST”) 800-171, NIST 800-53, the Payment Card Industry Data Security Standard (“PCI DSS”), International Organization for Standardization/International Electrotechnical Commission (“ISO/IEC”) 27001, or the Center for Internet Security (“CIS”) Critical Security Controls.

In some embodiments, the cybersecurity assessment system 120 may be configured to assess target networks for compliance with multiple distinct cybersecurity frameworks. Each cybersecurity framework may or may not share individual cybersecurity factors or subsets thereof with any number other cybersecurity frameworks. A single target network may be assessed for compliance with any or all of the cybersecurity frameworks available to the cybersecurity assessment system 120. For purposes of illustration, process 700 will be described with respect to assessment of a single target network 100 with respect to a single cybersecurity framework. Thus, at block 715, cybersecurity unit 140 identifies the cybersecurity framework associated with target network 100 (e.g., a database record associates the cybersecurity framework with target network 100), and loads the corresponding cybersecurity factors against which network 100 is to be evaluated.

FIG. 8 shows a portion of the cybersecurity framework against which target network 100 is being evaluated. The cybersecurity factors are organized into families 850A, 850B, and 850C. Each family 850A, 850B, and 850C may represent a particular grouping or commonalty of the associated cybersecurity factors. For example, family 850A includes controls 854A and 854B representing various aspects of cybersecurity preparedness. Family 850B includes controls 854C and 854D representing various aspects of cybersecurity preparedness. Family 850C includes controls 854E and 854F representing various aspects of cybersecurity preparedness.

Returning to FIG. 7 , at block 720, the cybersecurity unit 140 or some other component of the cybersecurity assessment system 120 may assign an initial score to each cybersecurity factor or control identified in block 715. As shown in FIG. 8 , the score associated with each control 854A-854F may comprise a binary representation “0” or “1,” wherein a 0 is assigned to the particular control if compliance with the control is negative, and a 1 is assigned if compliance is positive. For example, control 854A may correspond to the use of a firewall on target network 100. If there is a firewall being used on target network 100, then control 854A may initially be assigned a score of 1, as shown in FIG. 8 . Otherwise, if there is no firewall on target network 100, a score of 0 may initially be assigned to cybersecurity factor 854A.

In some embodiments, the initial score assigned to a cybersecurity factor may be based on user input or feedback. Cybersecurity unit 140 can use the provided user input to set initial scores during the process 700. For example, a user may be presented the list of cybersecurity factors using a graphical user interface (“GUI”), and provide manual assessment responses for each cybersecurity factor or some subset thereof using interactive controls of the GUI. As another example, the user may participate in an automated dialog with the cybersecurity assessment system 120, and may be prompted for assessment responses regarding the cybersecurity factors. The automated dialog may be in text form (e.g., using a text-based “chatbot”) or spoken form (e.g., using pre-recorded or synthesized speech for prompts). The cybersecurity assessment system 120 may process the user's responses using natural language processing (“NLP”) to identify the assessment responses for the prompted cybersecurity factors.

In some embodiments, the user input may be analyzed against machine learning models to determine initial scores for the cybersecurity factors and/or to provide feedback or guidance regarding cybersecurity factors. For example, a machine learning model may be trained or otherwise generated using data regarding cybersecurity factors observed from prior target networks during prior instances of the process 700. The model may be trained to map or otherwise classify user input (e.g., user responses regarding cybersecurity factors provided via a GUI or automated dialog) to particular scores for the cybersecurity factors. As another example, a machine learning model may be trained to map or otherwise classify user input and/or scores for cybersecurity factors to particular steps that may be taken to improve the scores, either before or after assessment against a cybersecurity framework. Detailed examples of machine learning modelling and use are described in greater detail below, and such models and methods may be used for cybersecurity factor and framework assessment in process 700. For example, vulnerability analysis and threat prediction may be applied or adapted to analyze user responses and determine cybersecurity factors of greatest concern from a cybersecurity framework compliance standpoint. As another example, threat analysis and remediation recommendation may be applied or adapted to analyze user responses, cybersecurity factor scores, and cybersecurity framework compliance results, and then provide guidance on actions that can be taken to improve cybersecurity framework compliance. The results of the analysis using such machine leaning models can be presented to a user. For example, if the user is participating in an automated dialog and providing input in response to prompts, the cybersecurity assessment system 120 may provide information regarding cybersecurity factors of concern, results of cybersecurity framework analysis, guidance on improving cybersecurity framework compliance, and the like in a dialog turn of the automated dialog.

In some embodiments, an initial score for a cybersecurity factor may be set to the value determined during the last execution of process 700 for the same cybersecurity framework and target network. In some embodiments, the scores for cybersecurity factors or some subset thereof may be initialized to 0, and may only be raised based on subsequent operations of process 700.

At block 725, the cybersecurity unit 140 or some other component of the cybersecurity assessment system 120 may adjust the initial scores for various cybersecurity factors based on scan data associated with the corresponding cybersecurity factor. In some embodiments, scores may be adjusted between a minimum and maximum threshold, such as 0 and 1 respectively. To determine the specific adjustment to be made for a particular cybersecurity factor, cybersecurity unit 140 may access scan data that relates to the cybersecurity factor, perform a rules-based analysis of the scan data, and generate a specific adjustment. In some embodiments, the rules-based analysis may be implemented as a series of rules, applied in a predetermined or dynamically determined sequence, in which a data value is evaluated to determine whether the data value satisfies a threshold or range for the particular data value. If the data value satisfies the criterion (or criteria) for a given rule, then the rules-based analysis may specify a particular outcome or additional rule to be applied; otherwise, the analysis may specific a different outcome or rule to be applied.

For example, control 854A may correspond to the presence of a firewall on target network 100. As shown and discussed above, an initial score of 1 has been assigned to control 854A, indicating that there is a firewall on target network 140. Cybersecurity unit 140 can access scan data associated firewall usage beyond the mere presence of a firewall. Illustratively, cybersecurity unit 140 can determine the vendor, model number, software version, and service level of the firewall based on information obtained from continuous monitoring data and vulnerability data. Cybersecurity unit 140 can then analyze the data against a series of rules, such as: Is the model number the most recent model produced by the vendor? If so, apply a first adjustment, otherwise apply a second adjustment. Is the model still supported by the vendor? If so, apply a first adjustment, otherwise apply a second adjustment. Is the software version the most recent software version available for the model? If so, apply a first adjustment, otherwise apply a second adjustment. Does the model have critical unresolved vulnerabilities? If so, apply a first adjustment, otherwise apply a second adjustment. Is the firewall (or set of firewalls) configured to manage all traffic between devices of target network 100 and the internet? If so, apply a first adjustment, otherwise apply a second adjustment. In some cases, no adjustment may be applied instead of the first and/or second adjustments in this example set of rules.

Adjustments to initial scores may take the form of weights that initial scores are multiplied by to determine a resulting adjusted score. In some embodiments, adjustments may take the form of values to be added to or subtracted from initial scores to determine a resulting adjusted score. In some embodiments, adjustments may take the form of parameters to a function that used to calculate an adjusted score.

The example rules and adjustment methods discussed herein are illustrative only, and are not intended to be exhaustive, required, or limiting in any way. In some embodiments, fewer, additional, and/or alternative rules and methods may be used to adjust scores for any or all controls.

In some embodiments, cybersecurity until 140 may also or alternatively evaluate non-scan-related data. For example, manual assessment data about certain characteristics may be considered, such as whether the password for the firewall has been changed from the default password. Such non-scan-related data may be used when determining adjustments to initial scores, or when determining the initial scores themselves.

In block 830, the cybersecurity assessment system may generate an overall score to represent the overall cybersecurity posture of the target network 100. As shown in overall score 866 in FIG. 8 , the adjusted scores of each individual control in list 862 are summed to generate the overall score 866. In some embodiments, the overall score may be a numerical value between a minimum and maximum threshold, such as 0 and 100 respectively. In this example, 0 indicates that no cybersecurity factor or control has been satisfied, and 100 indicates successful compliance with all cybersecurity factors for the current cybersecurity assessment framework.

In some embodiments, when the overall status score is presented (e.g., in portal interface 200 or cybersecurity status interface 300), the overall score may be associated with a color visually emphasizing the current cybersecurity status of the target network. For example, a perfect score of 100 may be associated with the color green, while a low score ranging from 0 to 25 may be associated the color red. Scores in intermediate ranges may be associated with other colors, such as the color yellow or orange.

At block 735, process 700 may end. Although the blocks of FIG. 7 are shown and described as occurring sequentially, in some embodiments certain blocks may be performed in a different order, in parallel, asynchronously, repetitively, etc. For example, blocks 710 and 715 may be performed concurrently or in a different order. As another example, blocks 715 and 720 may be performed currently with block 710.

Vulnerability Processing

FIG. 9 is a flow diagram of an illustrative process 900 for transforming a cybersecurity vulnerability scan file into mapped format files for interactive presentation. Process 900 begins at block 905. Process 900 may begin in response to an event, such as when scan data is generated by the aggregation unit 130 or upon receiving a command from a system administrator. In some embodiments, process 900 may be executed according to a predetermined or dynamically determined schedule. When process 900 is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable media (e.g., hard drive, flash memory, removable media, etc.) may be loaded into memory (e.g., random access memory or “RAM”) of a computing device. For example, transform instructions 2576 shown in FIG. 25 may be loaded into memory 2566 of a cybersecurity assessment system computing device 2550 and executed by one or more processors 2560. In some embodiments, process 900 or portions thereof may be implemented on multiple processors (on the same or separate computing devices), serially or in parallel.

At block 910, the transform unit 150 or some other component of cybersecurity assessment system 120 may receive a vulnerability data file comprising multiple data fields. For example, the transform unit 150 may receive vulnerability data which may be a large structured data file (e.g., a file that is several gigabytes or terabytes in size). In some embodiments, the vulnerability data file may be in the form of a comma separated value (“CSV”) file, tab delimited text file, JSON file, XML file, etc.

As shown in FIG. 10 , the received vulnerability data file 1000 may comprise a row of data for each detected vulnerability. Each row may have a plurality of data fields, such as an identifier for the device in which the vulnerability was detected (e.g., an IP address), a severity indicator for the vulnerability (e.g., critical, high, medium, low), an identifier for the vulnerability (e.g., a CVE number), a vulnerability name, description, recommended remediation, etc.

At block 915, the transform unit 150 or some other component of cybersecurity assessment system 120 may copy portions of the vulnerability data file 1000 into a plurality of separate files, separated by a top-level category that is used during display by a user interface such as the portal interface 200 or vulnerabilities interface 400. For example, the vulnerability data may be separated using a multi-tier hierarchy of categories corresponding to severity indicators for the corresponding vulnerabilities (e.g., critical, high, medium, etc.). In some embodiments, the plurality of top-level category files may be structured similarly to the vulnerability data file (e.g., CSV files, XML files, etc.).

As shown in FIG. 10 , in some embodiments, the plurality of top-level category files may comprise three severity files 1010, 1020, and 1030, wherein file 1010 is associated with a medium severity level, file 1020 is associated with a high severity level, and file 1030 is associated with a critical severity level. In some embodiments, the severity labels may be incorporated as data fields in the original vulnerability data file 1000 and may be used to generate the three separate severity files. Each severity file may include a set of data entries associated with the corresponding level of severity, and individual entries may include a vulnerability identifier, a vulnerability name, and a count of the number of instances of the vulnerability that have been detected (e.g., the number of devices affected by the vulnerability). Using these files, vulnerabilities interface 400 can present dynamic vulnerabilities tracker 418, which provides a visual representation of collected statistics regarding the vulnerabilities determined to be associated with a particular severity level.

At block 920, the transform unit 150 or some other component of cybersecurity assessment system 120 may scan the vulnerability data file for unique vulnerabilities to include in a map file. For example, the vulnerability data file 1000 may include a vulnerability identifier that appears multiple times throughout the vulnerability data file 1000. The transform unit 150 may extract data associated with the vulnerability identifier for entry into a map file 1040. In some embodiments, a key-value pair is created associating the vulnerability identifier with various other data elements, such as name, synopsis, description, and solution. If a particular vulnerability is already included in the map file 1040, then the transform unit 150 may skip the current record for that vulnerability without duplicating it in the map file 1040. Therefore, each unique vulnerability identifier will only appear once in the map file 1040 rather than appear perhaps hundreds of times as in the original vulnerability data file 1000. Advantageously, use of the map file 1040 may result in a condensed list of unique vulnerability identifiers, thereby reducing the computing resources required to store, load, process, or reference the map file 1040—in comparison with the much larger original vulnerability data file 1000—when performing other processes, such as generating user interfaces, performing cybersecurity assessments, providing exports of vulnerability data, etc.

In some embodiments, the map file 1040 may also include unique instances of other information, such as unique groups of device identifiers to which the unique vulnerability records in the map file 1040 apply. For example, there may be several (e.g., dozens, hundreds, or more) records in the vulnerability data file 1000 referencing a particular vulnerability identifier if the corresponding vulnerability was detected on multiple distinct devices. As discussed above, a single vulnerability record in the map file 1040 may replace the multiple duplicate copies of the same vulnerability in the vulnerability data file 1000. However, the device identifiers for each of the different devices affected by the vulnerability may be unique, and unable to be included in the single vulnerability record in the map file 1040. To ensure that each device affected by vulnerability can continue to be determined, the vulnerability record in the map file 1040 may be cross-referenced (e.g., associated with a pointer or unique key) to a listing of the unique identifiers for the different devices affected by the vulnerability. In some embodiments, the listing of unique device identifiers may also be stored in the map file 1040. In addition, if multiple vulnerabilities each affect the same group of devices, then multiple vulnerability records in the map file 1040 may be cross-referenced to the same listing of device identifiers, further conserving memory space and other computing resources in comprising with the original vulnerability data file 1000.

At block 925, the transform unit 150 or some other component of cybersecurity assessment system 120 may generate a summary file 1050 comprising a count of vulnerabilities by top-level category, such as severity. As shown in FIG. 10 , summary file 1050 includes a count of vulnerabilities for each severity level. The counts may be determined by counting the number unique instances of each vulnerability identifier and severity level identifier occurring together in the vulnerability data file 1000. Using the summary file 1050, an interface such as the portal interface 200 can present a summary of the vulnerabilities by severity type in the vulnerabilities option 222. As another example, vulnerabilities interface 400 can present chart 410 categorizing vulnerabilities according to severity level.

At block 930, transform unit 150 or some other component of cybersecurity assessment system 120 may store the generated files for later use in presenting user interfaces, performing cybersecurity assessments, and the like. Illustratively, the files 1010, 1020, 1030, 1040, and 1050 may be stored in data store 180. In some embodiments, the different files may not be stored as physically separate files, but may be integrated into a single file.

At block 935, process 900 may end. Although the blocks of FIG. 9 are shown and described as occurring sequentially, in some embodiments certain blocks may be performed in a different order, in parallel, asynchronously, repetitively, etc. For example, blocks 915, 920, and/or 925 may be performed concurrently or in a different order.

Hierarchical Target Networks

FIG. 11 shows multiple target networks 100A, 100B, 100C and a cybersecurity assessment system 120. Individual target networks 100A, 100B, 100C may include network infrastructure and any number of devices, as illustrated in FIG. 1 and discussed in greater detail above. Each of the target networks 100A, 100B, 100C may include the same or a different number or combination of devices as any other target network.

The cybersecurity assessment system 120 may include multiple separate instances to facilitate management of cybersecurity data and segregation thereof between different hierarchical groups of target networks. For example, one instance 1120A may be used to manage cybersecurity data for a top-level parent entity, such as a parent company with target network 100A. The parent entity may have a child entity, such as a subsidiary company with target network 100B. Another instance 1120B may be used to manage cybersecurity data for a supply chain that includes one entity, such as a company with target network 100C, that receives goods and/or services from another entity, such as the company with target network 100B. For ease of illustration and description, only three target networks and two instances are shown. In some embodiments, fewer, additional, and/or alternative target networks and/or instances may be implemented.

An instance of the cybersecurity assessment system 120 may include various components and data stores that provide some or all of the functionality of the cybersecurity assessment system 120 to a particular target network or hierarchical group of target networks. In some embodiments, an instance, such as instance 1120A, may include an application component 1122A that provides operational functionality of the cybersecurity assessment system 120. For example, the application component 1122A may provide the functionally described above with respect to the data stream units 125A, 125B, 125C, aggregation unit 130, cybersecurity unit 140, transform unit 150, user interface unit 160, etc. The application component 1122A may include software that can be provisioned onto one or more server computing devices as needed, or the application component 1102A may include a dedicated computing device or group computing devices.

The instance 1120A may also include various data stores. For example, the instance 1120A may include an object data store 1124A to store data objects (e.g., files of raw scan data, files of processed scan data, etc.). As another example, the instance 1120A may include a configuration data store 1126A to store data regarding the hierarchical structure of the target networks associated with the instance 1120A, access permissions for access data regarding the various target networks of the instance, data regarding the data objects in the object data store 1124A, and the like. Illustratively, the configuration data store 1126A may be or include a database, such as a relational database (e.g., a “SQL” database), a non-relational database (e.g., a “NoSQL” database), etc.

In some embodiments, different instances of the cybersecurity assessment system 120 may include different components and/or data stores. For example, instance 1120B may provide different functionality to one hierarchical group of target networks than instance 1120A provides to a different hierarchical group of target networks. The difference in functionality may be due to any of a variety of different factors, such as different service level agreements, different versions, different preferences of the entities for which the instance is being used to manage cybersecurity data, some combination thereof, etc. In such cases, application component 1122B may be different than application component 1122A (e.g., the code to be executed may be different, the hardware on which the code executes may be different etc.); object data store 1124B may be different than object data store 1124A (e.g., additional and/or alternative data objects may be obtained and stored based on different functionality of the instance); and/or configuration data store 1126B may be different than configuration data store 1126A (e.g., configuration data defining different access levels based on the different functionality of the instances, different structure or content of the data objects, etc.).

FIG. 12 shows various examples of target network hierarchies, and relationships between individual target networks and between target network hierarchies. Target network hierarchies may also be referred to as network hierarchies, or merely as hierarchies for convenience. Target network 1220 is shown as the top-level parent of hierarchy 1202. Target network 1240 is shown as the top-level parent of hierarchy 1204. Target network 1260 is shown as the top-level parent of hierarchy 1206. Each of the hierarchies and member target networks may use the services of the cybersecurity assessment system 120 to manage assessment of their cybersecurity status, vulnerabilities, events, and the like.

Hierarchies that use cybersecurity assessment system 120 to manage the cybersecurity information of the overall hierarchy and member target networks may have various configurations. For example, a hierarchy such as hierarchy 1202 may be a supply chain hierarchy in which goods and/or services are ultimately directed toward the entity associated with target network 1220. As shown, hierarchy 1202 includes target network 1222 as a child of target network 1220 and also as a parent of target network 1224. More specifically, the entities with which the target networks are associated may be the “child” and/or “parent.” For convenience in illustration and description, however, it is the target networks with which the entities are associated that will be described using the “child” and “parent” labels to indicate the overall structure of the hierarchy.

As another example, hierarchy 1204 may be a parent-subsidiary hierarchy. For example, the entity associated with target network 1240 may be a parent company with multiple subsidiaries, any of which may also be parent companies with their own subsidiaries, etc. As shown, hierarchy 1204 includes target networks 1242 and 1244 as children of target network 1240. Each of the children target networks 1242 and 1244 are also parents: target network 1242 is the parent of target networks 1246 and 1248; target network 1244 is the parent of target network 1250.

In some embodiments, a single target network may be included in multiple hierarchies. For example, a subsidiary of a company may be part of a parent-subsidiary hierarchy for the corporate structure to which the subsidiary belongs. The subsidiary may also be part of a supply chain, outside of its parent-subsidiary corporate structure. Target network 1250 is shown as an example of such a target network. Target network 1250 is a subsidiary of target network 1244 (which is a subsidiary of top-level parent target network 1240) and part of hierarchy 1204. However, target network 1250 is also a child of target network 1260, which is the top-level target network of hierarchy 1206. Hierarchy 1206 may be a supply chain hierarchy in which goods and/or services from various target networks—including target network 1250 and target network 1262—are directed toward top-level target network 1260.

Various access configurations may be used to control which target network cybersecurity information can be accessed at various levels of a hierarchy. For example, in a supply chain hierarchy such as hierarchy 1202, the top-level parent target network 1220 may access cybersecurity status scores and/or vulnerability data for each of the target networks 1222 and 1224, and target network 1222 may access the cybersecurity status scores and/or vulnerability data for target network 1224, as indicated by the arrows going down the hierarchy 1202. In the illustration, the arrows indicate the reach and direction of access (rather than, e.g., the flow of data which typically would go in the opposite direction). The top-level parent target network 1220 may also or alternatively access an overall cybersecurity status score and/or overall vulnerability data for the hierarchy 1202. Illustratively, an overall cybersecurity status score may be a composite (e.g., an average, weighted average, normalized score, etc.) of the cybersecurity status scores for each individual target network in the hierarchy. An example of a user interface for accessing composite cybersecurity status scores and vulnerability data for hierarchies, and/or cybersecurity status scores and vulnerability data for other target networks in a hierarchy, is shown in FIG. 14 and discussed in greater detail below.

Because entities that are part of a supply chain hierarchy such as hierarchy 1202 may not be formally related in a corporate structure (e.g., are not parent companies and subsidiary companies to each other), the access to other types of cybersecurity data may be limited or blocked. For example, the top-level parent target network 1220 may not access specific, low-level details regarding target networks 1222, 1224, such as detail regarding individual vulnerabilities, security events, and the like. In the illustration, this limited level of access is indicated by the dashed arrows (rather than, e.g., solid arrows, which are used to indicate a higher level of access).

In some embodiments, target networks that are not necessarily top-level target networks but are otherwise parents to other target networks in a hierarchy may also access cybersecurity status scores and/or vulnerability data for their descendant target networks. For example, target network 1222, which is both a child (of target network 1220) and a parent (to target network 1224), may have an access configuration as the top-level parent of its own “sub-supply chain” in which it is the top-level parent.

Hierarchy 1204 is shown using a different access configuration than hierarchy 1202. As shown, target network 1240, which is the top-level parent of hierarchy 1204, can access all of the cybersecurity status and vulnerability information regarding all of the child target networks in the hierarchy 1204. In the illustration, this comprehensive level of access is indicated by the solid arrows (rather than, e.g., dashed arrows, which are used to indicate a limited level of access). Target network 1240 may have such comprehensive access by virtue of its status as the top-level parent of the hierarchy 1204, by inheriting the access level of the children of target network 1240 with respect to their children, or based on some other selected or automatically determined access configuration.

In some embodiments, children may be permitted to access cybersecurity information of parents and/or siblings. For example, as shown, target networks 1242 and 1244 can access cybersecurity information of target network 1240. Target networks 1246 and 1250 can access cybersecurity information of target networks 1242 and 1244, respectively. The access may go up multiple levels, such as target network 1246 being permitted to access cybersecurity information of target network 1240. The level of access that children have to the cybersecurity information of their parents may be different (e.g., more limited) than the access the parents have to the children. In the illustration, a limited level of access is indicated by the dashed arrows going from certain children to certain parents in hierarchy 1204. In some embodiments, children (or other descendants) of a common parent (or other ancestor) may access cybersecurity information about each other. For example, target networks 1242 and 1244 can access cybersecurity information about each other as siblings.

In some embodiments, access to cybersecurity information—whether in a “downward” (parent-to-child) or “upward” (child-to-parent) direction—may be continuous through multiple levels of a hierarchy. A top-level parent, such as target network 1240, may not have a direct link within the hierarchy 1204 to a child, such as target network 1246, that is multiple levels down the hierarchy (e.g., target network 1246 is a child of target network 1242, which is itself a child of target network 1240). However, a target network may inherit the level of access that each of its children, the children of its children (e.g. “grandchildren”), and other descendants (for any number of levels) have. For example, target network 1240 may access the cybersecurity information of target network 1246 by inheriting the access level of target network 1242, which is between target networks 1240 and 1246. In some embodiments, a top-level parent or other target network may not only inherit the levels of access of its descendants, but may have different levels of access by virtue of its position within the hierarchy. For example, target network 1240 may have a higher level of access than its descendants, and that higher level of access may pass through all descendants to every target network in the hierarchy 1204, regardless of the level of access that descendants have across levels of the hierarchy 1204.

Access level inheritance and “pass-through” access may not be desirable in situations where a target network is part of multiple hierarchies. Permitting access to the cybersecurity information of the children, parents, siblings, and other family members of a target network from a different hierarchy can raise security concerns. For example, target network 1250 is part of patent-subsidiary hierarchy 1204, and also a member of supply chain hierarchy 1206. As a member of hierarchy 1204, the top-level parent—target network 1240—may be permitted to access cybersecurity information of target network 1250 and any child of target network 1250. As shown, the only child of target network 1250 is in supply chain hierarchy 1206. However, permitting target network 1240 to access the cybersecurity information of target network 1262, which is the child of target network 1250 in hierarchy 1206, may not be desirable because target network 1262 is in a different hierarchy altogether and is not a subsidiary of target network 1240. In addition, permitting target network 1250 to access the cybersecurity information of target network 1260, which is the parent of target network 1250 in hierarchy 1206, may not be desirable because target network 1260 is not part of a parent-subsidiary corporate structure that includes target network 1250. In such cases, the access level inheritance and “pass-through” access may be blocked at the logical boundaries 1212, 1214, 1216 of each hierarchy. In addition, to ensure that no unauthorized access is possible, the data of each hierarchy may be separated into physically separate locations, and managed using different instances of the cybersecurity assessment system 120. In this way, physical boundaries may be implemented. Cybersecurity information for target networks that are in multiple hierarchies can be copied into the instances of each hierarchy so that the cybersecurity information is accessible within the hierarchies to which the target network belongs, but no access to cybersecurity information for another network is possible through inadvertent or erroneous application of access configuration data. An example process for setting up hierarchies and copying cybersecurity data between instances is described in greater detail below with respect to FIG. 15 .

The example hierarchy structures, access levels, access directions, and other aspects of access configuration described above are illustrative only, and are not intended to be limiting. In some embodiments, different and/or additional structures, access levels, directions, and the like may be used. For example, within a parent-subsidiary hierarchy, each target network may be permitted to access cybersecurity information regarding each other target network. As another example, the access of any target network to any other target network in a hierarchy may be determined and assigned separately.

Hierarchical Cybersecurity Information Presentation

FIG. 13 is a flow diagram of an illustrative process 1300 for controlling access to cybersecurity information within a hierarchy of target networks. Process 1300 begins at block 1302. Process 1300 may begin in response to an event, such as when a user accesses the cybersecurity portal. When process 1300 is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable media (e.g., hard drive, flash memory, removable media, etc.) may be loaded into memory (e.g., random access memory or “RAM”) of a computing device. For example, access control instructions 2580 shown in FIG. 25 may be loaded into memory 2566 of a cybersecurity assessment system computing device 2550 and executed by one or more processors 2560. In some embodiments, process 1300 or portions thereof may be implemented on multiple processors (on the same or separate computing devices), serially or in parallel.

At block 1304, the application component or some other component of an instance of the cybersecurity assessment system 120 may receive a request to access cybersecurity information regarding a hierarchy of target networks. In some embodiments, the request may be initiated via a desktop or mobile device user interface, such as the cybersecurity portal interface described in greater detail above. For example, a user associated with the entity that maintains target network 1240 may have an account with the instance of the cybersecurity assessment system 120, or there may be a single account associated with the entity itself (e.g., shared among multiple users). The user may access the cybersecurity portal to view cybersecurity information regarding the target network 1240, and the other target networks in hierarchy 1204. The user may submit a request for information about the hierarchy 1204, such as by clicking a link on a home page of the cybersecurity portal, activating menu option in a mobile application, etc. The instance of the cybersecurity assessment system 120 can identify the current target network associated with the account from which the request was received.

At block 1306, the application component or some other component of the instance of the cybersecurity assessment system 120 may begin processing the descendants (e.g., children, grandchildren, etc., for any number of levels) of the current target network within the current hierarchy. For each descendant of the current target network, the application component may execute blocks 1308 and 1310, below. In this example, the current hierarchy is hierarchy 1204, the current target network is target network 1240, and the first descendant is target network 1242.

At block 1308, the application component or some other component of the instance of the cybersecurity assessment system 120 may determine the access level that the current target network has with respect to cybersecurity information of the descendant target network for the current iteration. For example, the application component may first determine the access level that target network 1240 has with respect to the cybersecurity information of target network 1242. In this example, target network 1240 may have a high degree of access to the cybersecurity information of target network 1242 by virtue of target network 1240 being the top-level parent of hierarchy 1204, which is a parent-subsidiary hierarchy. Illustratively, the high degree of access may allow target network 1240 to access high-level data (e.g., cybersecurity score, vulnerabilities summary), and also more granular data (e.g., individual vulnerabilities, security events, etc.). In some embodiments, a parent target network may not be permitted to access any cybersecurity information for certain children target networks or groups thereof.

At block 1310, the application component or some other component of the instance of the cybersecurity assessment system 120 may obtain the cybersecurity data for the descendant target network that the current target network is authorized to access, as determined above. For example, the application component may access the cybersecurity score, vulnerability data, cybersecurity event data, etc. that has been scanned and generated for target network 1242, as discussed in greater detail above.

The process 1300 may return to block 1306 for any remaining descendants of the current target network. For example, the application component may traverse a graph-based representation of the hierarchy 1204, such as a tree, and execute blocks 1304, 1306, and 1308 for each node (or a subset thereof) that descends from the current target network 1240. As shown in FIG. 12 , target network 1250 is part of hierarchy 1204, and is a descendant of current target network 1240. In addition, target network 1250 has a descendant node 1262. However, descendant node 1262 is in a different hierarchy: hierarchy 1206. Moreover, data objects for target network 1262, and the configuration data used to control access to the cybersecurity data for target network 1262, are in a different instance of the cybersecurity assessment system 120. Thus, during the process of traversing the tree representing hierarchy 1204, the application component may not be aware of existence of target network 1262 and thus does not access cybersecurity data for target network 1262.

At block 1312, the application component or some other component of the instance of the cybersecurity assessment system 120 may generate composite cybersecurity information. The composite cybersecurity information may be based on cybersecurity information accessed above for the descendants of the current target network. In some embodiments, the application component may generate a composite cybersecurity score using the individual cybersecurity scores for each of the descendant target networks. For example, the application component may determine an average of the cybersecurity scores for all descendants of the current target network. The average may be determined using or excluding the cybersecurity score of the current target network. In some embodiments, the application component may generate composite vulnerability data using the vulnerability data for each of the descendant target networks. For example, the application component may determine a combined list, count, and classification of vulnerabilities identified in all target network (or some subset thereof), rather than for only a single target network as described above. The composite vulnerability data may be determined using or excluding vulnerability data of the current target network.

At block 1314, the application component or some other component of the instance of the cybersecurity assessment system 120 may begin processing the ancestors (e.g. parents, parents of parents such as grandparents, etc., for any number of levels) of the current target network within the current hierarchy. For each ancestor of the current target network, the application component may execute blocks 1316 and 1318, below. Target network 1240, which has been used in the example thus far, does not have any ancestors because target network 1240 is the top-level parent of the hierarchy. Therefore, blocks 1316 and 1318 below will be described with reference to example that uses target network 1262 of hierarchy 1206 as the current target network and current hierarchy, respectively.

At block 1316, the application component or some other component of the instance of the cybersecurity assessment system 120 may determine the access level that the current target network has with respect to cybersecurity information of the ancestor target network for the current iteration. For example, the application component may first determine the access level that target network 1262 has with respect to the cybersecurity information of target network 1250. In this example, target network 1262 may have a limited degree of access to the cybersecurity information of target network 1250. Illustratively, the limited degree of access may be access to only high-level data, such as the cybersecurity score of the ancestor target network 1250. Target network 1262 may be blocked from accessing more granular data, such as individual vulnerabilities, security events, etc. In some embodiments, a child target network may not be permitted to access any cybersecurity information of any ancestors, or a portion of the child target network's ancestors.

At block 1318, the application component or some other component of the instance of the cybersecurity assessment system 120 may obtain the cybersecurity data for the ancestor target network that the current target network is authorized to access, as determined above. For example, the application component may access the cybersecurity score that has been generated for target network 1250, as discussed in greater detail above.

The process 1300 may return to block 1314 for any remaining ancestors of the current target network. For example, the application component may traverse a graph-based representation of the hierarchy 1206, such as a tree, and execute blocks 1314, 1316, and 1318 for each node (or a subset thereof) that is an ancestor of the current target network 1262. As shown in FIG. 12 , target network 1250 is part of hierarchy 1204 in addition to hierarchy 1206, and has an ancestor node 1244 in hierarchy 1204. Thus, it appears that target network 1244 is an ancestor of target network 1262. However, data objects for target network 1244, and the configuration data used to control access to the cybersecurity data for target network 1244, is in a different instance of the cybersecurity assessment system. Thus, during the process of traversing the tree representing hierarchy 1206, the application component may not be aware of existence of target network 1244 and thus does not access cybersecurity data for target network 1244. In some embodiments, as discussed above, a target network may be permitted to access cybersecurity information of siblings instead of, or in addition to, ancestors. In such cases, the traversal of the graph-based representation of the current hierarchy may accessing cybersecurity information for target networks of sibling nodes, descendants/ancestors of sibling nodes, etc.

At block 1320, the application component or some other component of the instance of the cybersecurity assessment system 120 may generate a user interface to present the cybersecurity information accessed above. The interface may present composite cybersecurity information for the entire current hierarchy or for target networks that the current target network is permitted to access. Alternatively, or in addition, the interface may present cybersecurity information for individual target networks, etc.

FIG. 14 illustrates an example of a user interface 1400 to present cybersecurity information for a hierarchy of target networks. In some embodiments, as shown, interface 1400 includes a navigation 1402 portion to navigate through different levels of a hierarchy, a composite display portion 1404 to display composite cybersecurity information for the hierarchy, and a detail display portion 1406 to display cybersecurity information for individual target networks in the current level of the hierarchy (e.g., the level selected using the navigation portion 1402). For example, the user interface 1400 may be displayed to a user associated with target network 1240 in hierarchy 1204. The composite display portion 1404 may display composite security information for the entire hierarchy 1204. The detail display portion 1406 may display cybersecurity information for individual networks in a particular level of the hierarchy 1204.

The composite display portion 1404 may include a dynamic cybersecurity status indicator 1440 that visually displays the degree to which the hierarchy as a whole, the current level of the hierarchy, or some other group of target networks of the hierarchy complies with a cybersecurity assessment framework. The dynamic cybersecurity status indicator 1440 includes a portion 1442 to present a cybersecurity status score. The score may be a composite score determined as discussed above. The composite display portion 1404 may also include a cybersecurity assessment framework compliance chart 1444 to illustrate the level of completion or satisfaction for each cybersecurity assessment framework control and/or family of controls. In some embodiments, the presentation of cybersecurity information in the composite display portion 1404 may be similar to the presentation in the real-time cybersecurity status interface for a single target network, as shown in FIG. 3 and discussed in greater detail above.

The detail display portion 1406 may display dynamic cybersecurity status indicators 1460, 1462, 1464 for each of the individual target networks at the current level of the hierarchy. For example, the user may have navigated (using navigation portion 1402) to the bottom level of hierarchy 1204. Dynamic cybersecurity status indicators 1460, 1462, 1464 may thus present cybersecurity status of target network 1246, 1248, and 1250, respectively. When a user navigates to a different level of the hierarchy, such as by selecting a different option of the navigation portion 1402, then number of dynamic cybersecurity status indicators shown in detail display portion 1406, and the specific cybersecurity statuses reflected thereby, may be changed to present information for the newly selected level of the hierarchy.

Although the user interface 1400 is shown as providing information about only cybersecurity assessment status, the user interface 1400 may also or alternatively show other cybersecurity information. In some embodiments, the individual dynamic cybersecurity status indicators may be replaced by, or presented in combination with, vulnerabilities trackers like those shown in FIG. 4 . For example, a composite vulnerabilities tracker may be presented in the composite display portion 1404 to track all vulnerabilities of the hierarchy, and vulnerabilities trackers for individual target networks may be presented in the detail display portion 1406. In some embodiments, the individual dynamic cybersecurity status indicators may be replaced by, or presented in combination with, tickers, event charts, etc. as shown in FIG. 5 for displaying individual cybersecurity events.

Returning to FIG. 13 , at decision block 1322 a user may have selected an option to “drill down” or view more information regarding a particular target network. For example, the dynamic cybersecurity status indicators (or other presented controls) in the detail display portion 1406 may be selectable. Selection of a particular dynamic control may initiate a “drill down” request to access additional information about a corresponding target network. If such a request is received at decision block 1322, the process 1300 may proceed to block 1324. Otherwise, if no such request is received, the process 1300 may terminate at block 1326.

At block 1324, the application component or some other component of the instance of the cybersecurity assessment system 120 may generate a user interface with cybersecurity information regarding a target network associated with the “drill down” request received above. The application component may determine which target network is represented by or otherwise associated with a control selected by the user. The application component may then generate a user interface with target-network-specific information, such as a real-time cybersecurity status interface as shown in FIG. 3 , a vulnerabilities interface as shown in FIG. 4 , or a real-time cybersecurity event ticker interface as shown in FIG. 5 . After presenting the user interface, the process 1300 may return to decision block 1322.

Target Network Setup and Maintenance in Hierarchy

FIG. 15 is a flow diagram of an illustrative process 1500 for adding a target network to a hierarchy and maintaining cybersecurity information for the target network. Process 1500 begins at block 1502. Process 1500 may begin in response to an event, such as when a user initiates the process of adding a target network to a hierarchy. When process 1500 is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable media (e.g., hard drive, flash memory, removable media, etc.) may be loaded into memory (e.g., random access memory or “RAM”) of a computing device. For example, access control instructions 2580 shown in FIG. 25 may be loaded into memory 2566 of a cybersecurity assessment system computing device 2550 and executed by one or more processors 2560. In some embodiments, process 1500 or portions thereof may be implemented on multiple processors (on the same or separate computing devices), serially or in parallel.

At block 1504, the application component or some other component of an instance of the cybersecurity assessment system 120 may receive a request to add a target network to the hierarchy associated with the instance.

At block 1506, the application component or some other component of the instance of the cybersecurity assessment system 120 may generate a storage configuration for the target network. Generating the storage configuration may include defining what cybersecurity data will be stored for the target network (e.g., in data objects), where the data objects will be stored (e.g., where within an object data store of the instance), etc. Data regarding the storage configuration may be stored in a configuration data store of the instance.

At block 1508, the application component or some other component of the instance of the cybersecurity assessment system 120 may generate an access configuration for the target network. Generating the access configuration may include defining what cybersecurity information of the target network can be accessed by other target networks in the hierarchy. For example, access to the cybersecurity information of the newly-added target network may be defined on a network-by-network basis, whereby each other target network in the hierarchy is separately given a particular level of access. In some embodiments, there may be default access levels. For example, a top-level parent of a parent-subsidiary hierarchy may automatically be given a high level of access to all target networks of the hierarchy, while other target networks may only be given limited access, and then only if they are direct parents of the newly-added target network. Additional or alternative default levels of access and rules for providing the default levels of access may be used depending upon the specific requirements of the hierarchy.

Generating the access configuration may also or alternatively include defining which other target networks of the hierarchy may be accessed by the newly-added target network. The access level may be assigned separately on a network-by-network basis, or it may be assigned using defaults as discussed above.

At decision block 1510, the application component or some other component of the instance of the cybersecurity assessment system 120 may determine whether the target network is also in a different hierarchy managed by a different instance of the cybersecurity assessment system 120. When a target network is in multiple hierarchies, the process of obtaining, processing, and maintaining cybersecurity scan information may differ from hierarchy to hierarchy. If the target network is not in any other hierarchy, the process 1500 may proceed to block 1512. Otherwise, if the target network is in a different hierarchy already, the process 1500 may proceed to block 1514.

At block 1512, the application component or some other component of the instance of the cybersecurity assessment system 120 may initiate a scan of the target network to obtain cybersecurity information, as described in greater detail above. The process 1500 may then proceed to block 1516 where the scan data may be stored in the object data store for the instance.

At block 1514, if the target network is already in a different hierarchy, the application component or some other component of the instance of the cybersecurity assessment system 120 may obtain data objects for the target network from another instance associated with the other hierarchy to which the target network belongs. The process 1500 may then proceed to block 1516 where the data objects may be stored in the object data store for the instance.

FIG. 16 is a block diagram illustrating different methods of obtaining scan data for the target network. Initially, target network 1600 may be added to a hierarchy that is managed by instance 1602 of the cybersecurity assessment system 120. The instance 1602 may initiate a scan of the target network 1600, and store the scan data as one or more data objects 1622 in object data store 1620.

Subsequently, the target network 1600 may be added to a different hierarchy, managed by instance 1604 of the cybersecurity assessment system. Rather than running the cybersecurity scan of the target network again (or twice for each time the scan is scheduled to be run), the instance 1604 may obtain the data objects 1622 from the object data store 1620 of instance 1602, and copy the data objects 1622 into the object data store 1640 of the instance 1604. Once copied to the object data store 1640 of the instance 1604, the data objects 1622 can be accessed and used as though the instance 1604 performed the scan.

Returning to FIG. 15 , at block 1518 the application component or some other component of the instance of the cybersecurity assessment system 120 may process the scan data, access requests, and the like. The operations may be processed based on the configuration data for the target network stored in the configuration data store, as described in greater detail above.

At decision block 1520, the application component or some other component of the instance of the cybersecurity assessment system 120 may determine whether new scan data is to be obtained for the target network. For example, scans may be run continuously, on a schedule, or on demand. If so, the process 1500 may return to block 1512 or 1514, depending upon the configuration of the target network and instance. Otherwise, the process 1500 may terminate at block 1522.

Cybersecurity Threat Collection, Integration, or Remediation

Conventional approaches to addressing cybersecurity issues include software management through human interaction and assessment of anomalies identified by cybersecurity applications. These conventional approaches may be a part of a typical security operations center (“SOC”) paradigm. Human personnel may continuously monitor cybersecurity applications, such as monitoring 24 hours a day, 7 days a week. The monitoring can include reviewing application state, application performance, or application reporting. The personnel can analyze data, determine actions based upon the data, take some action, or create logs to record the action taken. However, such conventional approaches to cybersecurity monitoring can be associated with a number of costs, inefficiencies, or undesirable effects.

An improved cybersecurity system can generate insights that may not be possible by human personnel, enable automated cybersecurity responses more quickly and consistently than would be possible by human personnel, and otherwise improve the secure operation of a target network. In some aspects, tasks that were associated with personnel in conventional systems can be replaced with automated processes. The cybersecurity system can generate insights, take actions associated with the insights, or automatically escalate to a subject matter expert for defined escalation roles. The cybersecurity system can monitor, detect, or act upon the insights with context specific actions. The cybersecurity system can be or can include elements of the cybersecurity assessment system 120 of FIG. 1 .

The cybersecurity system can enable entities to receive cybersecurity threat data, such as cyber threat intelligence (“CTI”) and indicators of compromise (“IOC”). The cybersecurity threat data can identify malicious Internet Protocol addresses, hashes, or compromised domains. The cybersecurity system can include a cybersecurity exchange. The cybersecurity exchange can implement a Trusted Automated Exchange of Intelligence Information (“TAXII™”) server. The cybersecurity system can also use Structured Threat Information Expression (“STIX™”), which is a language and serialization format used to exchange cybersecurity threat data. The cybersecurity system or exchange can also conform to an OASIS® specification for the TAXII™ server or the STIX™ data, such as the TAXII™ V1.1.1 OASIS Committee Specification, TAXII™ V2.0 OASIS Committee Specification, STIX™ V1.2.1 OASIS Committee Specification, or STIX™ V2.0 OASIS Committee Specification. Feeds from disparate security applications and other data sources such as SIEM data sources, event data sources, CCM sources, intrusion detection systems (“IDS”), or other data sources can be integrated into the cybersecurity exchange. Normalized cybersecurity threat data from other entities can be consolidated, analyzed, or provided as a threat feed to individual entities for remediation. As used herein, the term “TAXII server” may be used interchangeably with “exchange server” and may refer to a server that uses another transport protocol than TAXII™; similarly, the term “TAXII client” may be used interchangeably with a “cybersecurity exchange client” or “exchange client” and may refer to a client that uses another transport protocol than TAXII™. As used herein, the term “STIX” may be used interchangeably with any cybersecurity data format.

The cybersecurity exchange can include an application programming interface (“API”). The cybersecurity exchange's API can (i) implement a client-server architecture, (ii) be stateless, (iii) be cacheable, (iv) include a uniform interface, (v) include a layered system, and/or (vi) allow client functionality to be extended by downloading and executing code in the form of applets or scripts. In other words, the cybersecurity exchange's API can be RESTful. The cybersecurity exchange can enable an exchange of cybersecurity threat data, such as cyber threat intelligence, indicators of compromise, or data objects, in a request response model. The cybersecurity exchange can enable producers to push cybersecurity threat data in a particular format (such as STIX™) to one or more consumers. The cybersecurity exchange can receive data from open sources or closed sources.

The cybersecurity exchange can be integrated with a cybersecurity system. The cybersecurity exchange clients can be installed at consumer endpoints (e.g., on target networks) for integration into the cybersecurity system. The cybersecurity system can present the cybersecurity threat data (such as data objects) according to a request response model. The cybersecurity system can also present request and response, hosted threat feeds.

The cybersecurity exchange can facilitate near-time or real-time sharing of cybersecurity threat data. The cybersecurity threat data can be shared in a feed in a particular format (such as STIX™). The cybersecurity threat data can include metadata that defines a category of the threat data. An example category is the Traffic Light Protocol (“TLP”). Categories under TLP include: RED—restricted to only participants or named recipients; AMBER—limited distribution or restricted to participants' organizations; GREEN—community sharing is permitted; and WHITE—unlimited disclosure.

The cybersecurity system can be a cloud-based system, such as by being implemented by one or more clustered instances (such as virtual machines) implemented in a hosted computing environment. The hosted or cloud computing environment can provide scalability without failure due to overload and saturation.

The cybersecurity system can ingest received data, such as cybersecurity threat data received from the cybersecurity exchange. The ingested data can be processed, normalized, and/or stored, and can then be presented in or more user interfaces of the cybersecurity system. The ingested data can be stored in object databases, non-relational databases (e.g., NoSQL databases, etc.), or in-memory databases. The databases can be in a hosted storage environment that includes a collection of physical data storage devices that may be remotely accessible and may be rapidly provisioned as needed (commonly referred to as “cloud” storage). In some embodiments, the ingested data can be stored in relational databases (e.g., Oracle databases, PostgreSQL databases, etc.), document-oriented databases (e.g., MongoDB), or other databases. Ingested data can be obfuscated so that entity-specific details are anonymized. Cybersecurity threat data, such as threat feeds, can be maintained in near-time, real-time, or historical feeds. In some embodiments, the cybersecurity databases or portions thereof can be immutable. For example, portions of the cybersecurity databases can use blockchain technology. Blockchain can be leveraged to maintain facts or solutions (such as threats or remediations) in an immutable manner in a database.

Data ingested into the cybersecurity system can be analyzed to categorize and separate data for utilization. The cybersecurity system can use artificial intelligence, such as machine learning, to perform data analytics, data correlation, categorization, and/or enrichment of cybersecurity threat data. A service can authenticate a threat as a legitimate actionable insight. Once a cybersecurity threat has been verified, a remediation or knowledge article can be authored with artificial intelligence or by a subject matter expert. The generated information can be added to a blockchain. A social networking context of sentiment analysis can be collected to enable an extended corpus of event decisions to improve performance of solutions to insights.

Cybersecurity Threat Graphical User Interfaces

The cybersecurity system can present graphical user interfaces. The graphical user interfaces can include cybersecurity threat data, such as threat feeds, cybersecurity alerts, vulnerability data, situational awareness data, continuous monitoring data, trends data, and/or specific malware incidents. As described herein, the cybersecurity threat data can be received from the cybersecurity exchange. The graphical user interfaces can include dashboards, visualizations, widgets, or reports. The graphical user interfaces can present threat data, global trends, or business sector specific data. The graphical user interfaces can use access controls, such as role-based access controls. With the access controls, the graphical user interfaces can be customized for particular entities, administrators, staff, or members. Data presented in the graphical user interfaces can be downloaded or exported by users. Individualized reports can also be viewed, generated, or downloaded according to allowed privileges. The cybersecurity system can provide graphical user interfaces or configurations to define granular access controls, such as user roles and responsibilities.

Cybersecurity System and Exchange

FIG. 17A shows an example network environment in which aspects of the present disclosure may be implemented. As shown, the network environment 1700 may include a cybersecurity system 1720 and one or more producers or consumers 1706, 1710A, 1710B of cybersecurity data. The cybersecurity system 1720 and the one or more producers or consumers 1706, 1710A, 1710B may communicate with each via one or more networks 115. The cybersecurity system 1720 may be, or can include similar element(s) as, the cybersecurity assessment system 120 of FIG. 1 or the cybersecurity assessment system computing device 2550 of FIG. 25 .

The cybersecurity system 1720 can include a cybersecurity exchange 1702. The cybersecurity exchange 1702 can facilitate the exchange of cybersecurity data. The cybersecurity exchange 1702 can include an exchange server 1704. As described herein, the cybersecurity exchange 1702 can follow the TAXII™ protocol. An example exchange server 1704 can be a TAXII™ server. The cybersecurity exchange 1702 can receive cybersecurity data that is published from one or more producers 1706. The cybersecurity exchange 1702 can host the cybersecurity data. The cybersecurity exchange 1702 can follow a request-response model, which can be over HTTPS.

The one or more producers or consumers 1706, 1710A, 1710B can each include an exchange client 1708A, 1708B, 1708C. The exchange client(s) 1708A, 1708B, 1708C can be configured to communicate with the exchange server 1704. The example exchange client(s) 1708A, 1708B, 1708C can be TAXII™ clients. The exchange client(s) can exchange information with other exchange client(s) in a publish-subscribe model. The cybersecurity exchange 1702 can use one or more channels. A channel can allow the producer(s) 1706 to push data to one or more consumers 1710A, 1710B and consumers 1710A, 1710B to receive data from one or more producers 1706.

The cybersecurity exchange 1702 can support the exchange of cybersecurity data in a STIX™ format. In some embodiments, the cybersecurity exchange 1702 can also be used to share data in other formats. In other words, the cybersecurity exchange 1702 can be used to transport non-STIX data. Sharing of data using the STIX format does not rely on any specific transport mechanism.

FIG. 17B shows an example cybersecurity system 1720 and various illustrative components thereof. The cybersecurity system 1720 can include a threat data store 1740. The cybersecurity data received from one or more external partners (e.g., producers 1706 and/or consumers 1710), such as threat data 1730 received from one or more exchange clients 1708, can be stored in the threat data store 1740. The cybersecurity system 1720 can process cybersecurity data from the threat data store 1740 by performing threat data analysis. For example, the cybersecurity system 1720 may include a cybersecurity artificial intelligence and machine learning (“AI/ML”) service 1742 to analyze cybersecurity data and predict threats, recommend remediations, and the like, as described in greater detail below. The cybersecurity system 1720 can also include at least a partially automated SOC 1744 to monitor applications to identify a possible cyber-attack or intrusion events.

The cybersecurity system 1720 can process cybersecurity data or threats by performing threat data categorization 1746. A severity or criticality of threats or business entities impacted can be determined. The cybersecurity system 1720 can further perform data normalization on the cybersecurity data or threats. The categorized or processed data can be presented in user interfaces 1748, such as customized dashboards or reports. The categorized or processed data can also be shared in a threat feed 1750. As described herein, the categorized or processed data may in some embodiments be in a STIX™ format or can follow the Traffic Light Protocol (“TLP”).

FIG. 17C shows an example partner 1712, which may be a producer or consumer. The partner 1712 may generate and store cybersecurity data, such as event data 2880, threat ticketing data 1772, threat solution data 1774, or the like. In some embodiments, the cybersecurity data may be stored at the partner 1712 in one or more cybersecurity data stores 1760. The cybersecurity data can be in the STIX format. As shown, the partner 1712 can include an exchange client 1708, which can include inbox or polling services to receive or send data to or from an exchange server 1704. For example, the partner 1712 can receive one or more threat feeds 1750 and/or transmit threat data 1730.

Cybersecurity Machine Learning

FIG. 18A shows an example cybersecurity AI/ML service 1742 in which aspects of the present disclosure may be implemented. The cybersecurity AI/ML service 1742 can receive various input data and, based thereon, train one or more machine learning models for use in various cybersecurity threat analysis tasks. Example input data can include, but is not limited to, any of the cybersecurity data described herein, such as event data, vulnerability data, scan data, log files, threat data received from the cybersecurity exchange 1702, or some combination thereof. As described herein, a cybersecurity machine learning model 1808 can be generated from machine learning techniques, such as supervised, semi-supervised, or unsupervised machine learning. In some embodiments, a cybersecurity machine learning model 1808 can be a classification model (e.g., capable of identifying a category, type, or use case) or a regression model (e.g., capable of determining a numerical value). Thus, output data of the model 1808 can be an output classification or an output value. The model 1808 may be or include a Bayesian classifier, neural network, support vector machine, decision tree, or other machine learning model capable of being applied to the types of data described herein.

In some embodiments, as shown, the cybersecurity AI/ML service 1742 may receive input data from the threat data store 1740. The input data form the threat data store 1740 may include various items of threat data regarding threats remediated or observed in various target networks. The threat data store 1740 may be populated, at least in part, by threat data received from various partners of the cybersecurity exchange 1702. For example, “tickets” or other records of threats detected and addressed may be stored in the threat data store 1740 and accessed by the cybersecurity AI/ML service 1742. The input data may be ingested into the cybersecurity AI/ML service 1742, which may perform several operations on the input data to train or otherwise generate one or more models 1808. In some embodiments, the cybersecurity AI/ML service 1742 may perform the operations shown in blocks 1802, 1804, and 1806 (or some subset thereof) process input data and train one or more models 1808.

At block 1802, the cybersecurity AI/ML service 1742 may preprocess the input data. Preprocessing the input data may involve one or more operations, such as filtering, transforming, normalizing, other processing operations, or some combination thereof. For example, cybersecurity AI/ML service 1742 may normalize variances in threat data, select representative remediations, and the like. Specific, non-limiting examples of preprocessing input data in the context of training illustrative models are shown in FIGS. 18B and 18C and described in greater detail below.

At block 1804, the cybersecurity AI/ML service 1742 may generate training data from which a model 1808 is to be trained. Generating training data may involve one or more operations on the preprocessed input data (or raw input data, if not preprocessed), such as augmenting the preprocessed input data with other data, structuring the data as training data, other operations, or some combination thereof. For example, the cybersecurity AI/ML service 1742 may obtain cybersecurity data regarding target networks to augment the threat data, generate model input data structures such as vectors or arrays, and the like. The cybersecurity data regarding target networks may be obtained from one or more target network cybersecurity data stores 1800, and may include data regarding the cybersecurity posture of target networks contemporaneous with the corresponding observed threats to be represented by the training data. Specific, non-limiting examples of generating training data in the context of training illustrative models are shown in FIGS. 18B and 18C and described in greater detail below.

At block 1806, the cybersecurity AI/ML service 1742 may train a model 1808 using the training data. Training a model 1808 may involve a variety of different operations, depending upon the particular type of model being trained. For example, if the model 1808 is a naïve Bayesian classifier, then training may include analyzing features of the training data to determine various statistical properties of the training data. These statistical properties can be used as parameters of a probabilistic Bayesian classifier, such as a multinomial naïve Bayesian classifier or a Gaussian naïve Bayesian classifier. As another example, if the model 1808 is a neural network, then training may include an iterative trial and error process in which input data is analyzed using a current set of model parameters (which may, in the initial iteration, be set to random values) and analyzed against a reference output using an objective function. Based on objective function analysis, parameters of the model may be adjusted such that in the next iteration the output will be closer to the reference output. The iterations may continue until a stopping criterion is reached (e.g., the average performance across a number of test inputs satisfies a threshold). The example training methods described herein are illustratively on, and are not intended to be limiting.

The output of the training process performed by the cybersecurity AI/ML service 1742 may be one or more models 1808 that may be used to provide various services to target networks. In some embodiments, as shown in FIG. 18B and described in greater detail below, the model 1808A may be a threat analysis model used to provide insight regarding the threat exposure of target networks given their current cybersecurity posture as represented by scan data, vulnerability data, etc.

As shown in FIG. 18B, input data 1810 may represent various aspects of threats observed in target networks or other partners. Illustratively, the input data may include: an identifier of a support ticket or other record of threat data; one or more timestamps representing when a threat was detected, when the ticket was generated, when a remediation was performed, etc.; an identifier or classification of the type of threat; a description of the threat; an identifier of the target network in which the threat was detected; IP addresses of devices affected by or otherwise associated with the threat, such as a source IP address and/or a destination IP address; and an identifier and/or description of the remediation—if any—that was performed.

The input data 1810 may be preprocessed at block 1802 to generate preprocessed data 1812. Preprocessing may involve normalizing some or all of the data fields in the input data 1810 to facilitate comparison, analysis, summarization, and the like during generation of the training data and training of the model 1808A. Illustratively, IP addresses may be formatted in a standard way, minor inconsistencies in otherwise equivalent values may be standardized, and/or a representative remediation may be selected and applied to all instances of a particular threat.

The cybersecurity AI/ML service 1742 may generate training data 1814 at block 1804 from the preprocessed data 1812. Generating training data 1814 may include augmenting the preprocessed data 1812 with data regarding the target network(s) in which the threats represented by the preprocessed data 1812 were detected. For example, for each threat record of the preprocessed data 1812, the cybersecurity AI/ML service 1742 may obtain vulnerability data, scan data, or the like for the target network in which the threat was detected from a time associated with the threat (e.g., when the threat was detected, when the threat originated, when a remediation was implemented, etc.). The cybersecurity AI/ML service 1742 may then generate training data 1814 by adding target-network-specific cybersecurity data to the threat data for analysis during training. For example, the cybersecurity AI/ML service 1742 may generate an array with summary data regarding a threat, and data regarding the contemporaneous vulnerabilities of the target network (or a subset thereof, such as the most common vulnerabilities detected across the target network, the vulnerabilities characterized as the most critical, or vulnerabilities satisfying some other selection criterion). An illustrative training data array may include an identifier of a particular threat (which may be mapped to descriptive information about the threat), and identifiers of one or more selected vulnerabilities of the target network (which may be mapped to descriptive information about the vulnerabilities).

The cybersecurity AI/ML service 1742 may train a threat prediction model 1808A using the training data 1814 at block 1806. In some embodiments, the threat prediction model 1808A may be a multinomial naïve Bayesian classifier configured to receive input data regarding vulnerabilities of a target network and generate output data representing one or more threats most likely to be experienced by the target network with the given vulnerabilities. Illustratively, the cybersecurity AI/ML service 1742 may determine statistical and probabilistic properties of the training data with respect to particular threats, such as the probability of particular vulnerabilities being present given the occurrence of a particular threat. From these probabilistic properties and Bayesian probability modelling, the cybersecurity AI/ML service 1742 can derive a model 1808A that produces the probability of a particular threat occurring given a particular vulnerability or set of vulnerabilities.

An example process 1900 for using a threat prediction model 1808A is shown in FIG. 19A. The cybersecurity AI/ML service 1742 may retrain the threat prediction model 1808A periodically or in response to an event. For example, the cybersecurity AI/ML service 1742 may retrain the threat prediction model 1808A every n units of time, wherein n is any number and the units may be days, weeks, months, quarters, etc. As another example, the cybersecurity AI/ML service 1742 may retrain the threat prediction model 1808A after a threshold number of threats are observed, after a threshold amount of threat data is obtained, after a significant threat is detected, etc.

In some embodiments, as shown in FIG. 18C and described in greater detail below, the model 1808B may be a remediation recommendation model used to provide a recommended remediation for a particular threat experienced by a target network.

As shown in FIG. 18C, input data 1810 may represent various aspects of threats observed in target networks or other partners. Illustratively, the input data may include: an identifier of a support ticket or other record of threat data; one or more timestamps representing when a threat was detected, when the ticket was generated, when a remediation was performed, etc.; an identifier or classification of the type of threat; a description of the threat; an identifier of the target network in which the threat was detected; IP addresses of devices affected by or otherwise associated with the threat, such as a source IP address and/or a destination IP address; and an identifier and/or description of the remediation—if any—that was performed.

The input data 1810 may be preprocessed at block 1802 to generate preprocessed data 1812. Preprocessing may involve normalizing some or all of the data fields in the input data 1810 to facilitate comparison, analysis, summarization, and the like during generation of the training data and training of the model 1808B. Illustratively, IP addresses may be formatted in a standard way, minor inconsistencies in otherwise equivalent values may be standardized, and/or a representative remediation may be selected for a particular threat.

The cybersecurity AI/ML service 1742 may generate training data 1814 at block 1804 from the preprocessed data 1812. For example, the cybersecurity AI/ML service 1742 may generate an array with summary data regarding a threat and remediation thereof, such as a source IP address, a destination IP address, an attack name or identifier, and an identifier of the representative resolution for the attack.

The cybersecurity AI/ML service 1742 may train a remediation recommendation model 1808B using the training data 1814. In some embodiments, the remediation recommendation model 1808B may be a Gaussian naïve Bayesian classifier configured to receive input data regarding a threat and generate output data representing one or more recommended remediations for the threat. Illustratively, the cybersecurity AI/ML service 1742 may determine statistical and probabilistic properties of the training data with respect to particular remediations, such as the probability of particular threat characteristics being present for a threat given the use of a particular remediation. From these probabilistic properties and Bayesian probability modelling, the cybersecurity AI/ML service 1742 can derive a model 1808B that produces the probability of a particular remediation being used given a particular set of threat features.

An example process 1950 for using a remediation recommendation model 1808B is shown in FIG. 19B. The cybersecurity AI/ML service 1742 may retrain the remediation recommendation model 1808B periodically or in response to an event. For example, the cybersecurity AI/ML service 1742 may retrain the remediation recommendation model 1808B every n units of time, wherein n is any number and the units may be days, weeks, months, quarters, etc. As another example, the cybersecurity AI/ML service 1742 may retrain the remediation recommendation model 1808B after a threshold number of threats are observed, after a threshold amount of threat data is obtained, after a significant threat is detected, etc.

Threat Prediction

FIG. 19A is a flow diagram of an illustrative process 1900 for determining the threat or threats that may be most likely to be experienced by a target network based on the current cybersecurity posture of the target network, such as the vulnerabilities most-recently identified for the target network. The process 1900 begins at block 1905. Illustratively, process 1900 may begin automatically in response to an event or on a predetermined or dynamically-determined schedule. For example, process 1900 may be initiated in response to a system administrator of a target network accessing a user interface to obtain threat information.

At block 1910, the cybersecurity AI/ML service 1742 may receive a request for threat prediction for a subject target network. For example, a system administrator of a target network may request a determination of which threat or set of threats the target network is most likely to experience, given the current state of the target network.

At block 1915, the cybersecurity AI/ML service 1742 may obtain data regarding the current cybersecurity posture of the target network. In some embodiments, the data may be vulnerability data representing the vulnerabilities, if any, that are currently experienced by the target network or that have otherwise been most-recently detected for the target network.

At block, 1920, the cybersecurity AI/ML service 1742 may generate input data for the threat prediction model 1808A. Illustratively, the input data may represent the vulnerabilities and/or other cybersecurity features of the target network based on the cybersecurity data obtained for the target network. For example, the input data may be a vector or array of identifying data (e.g., numerical identifiers) for the vulnerabilities most-recently detected for the target network, or some subset of the vulnerabilities (e.g., the most common, the most critical, etc.).

At block 1925, the cybersecurity AI/ML service 1742 may generate output data from the threat prediction model 1808A using the input data. Illustratively, the output data may include probabilities of particular threats being experienced by the target network based on the input data provided to the model 1808A. For example, the threat prediction model 1808A may produce probabilities for each possible threat that the model has been trained to analyze, probabilities for the n highest-probability threats that the model 1808A has produced for the target network, the highest-probably threat, or the like.

At block 1930, the cybersecurity AI/ML service 1742 may provide information regarding the determined threats. The information may identify the highest-probability threat(s), the probabilities, recommended remediations, some combination thereof, etc. Illustratively, the information regarding the determined threats may be presented in any of a variety of modalities, such as: text-based and/or graphic-based presentations via a GUI; text-based output provided via a chatbot; audio output via a voice-based system using pre-recorded or synthesized speech; etc.

Remediation Recommendation

FIG. 19B is a flow diagram of an illustrative process 1950 for determining remediations for threats based on features of the threats and remediations that have been used previously to address the threats. The process 1950 begins at block 1955. Illustratively, process 1950 may begin automatically in response to an event or on a predetermined or dynamically-determined schedule. For example, process 1950 may be initiated in response to a system administrator of a target network accessing a user interface to obtain threat information.

At block 1960, the cybersecurity AI/ML service 1742 may receive a request for a remediation recommendation for a particular threat. For example, a system administrator of a target network may enter or select an open threat support ticket. As another example, a batch or automatic process may process entered or open threat support tickets and request a remediation recommendation.

At block 1965, the cybersecurity AI/ML service 1742 may generate input data for the remediation recommendation model 1808B. Illustratively, the cybersecurity AI/ML service 1742 may determine features of the threat for which a remediation is to be recommended. In some embodiments, the features may include IP address information for the threat (e.g., source IP address and destination IP address) and threat identifying information (e.g., name or identification number).

At block 1970, the cybersecurity AI/ML service 1742 may generate output data from the remediation recommendation model 1808B using the input data. Illustratively, the output data may include probabilities of particular remediations being used for threats having the threat features input to the model 1808B. For example, the remediation recommendation model 1808B may produce probabilities for each possible remediation that the model has been trained to analyze, probabilities for the n highest-probability remediation recommendations that the model 1808A has produced for the threat, the highest-probably remediation recommendation, or the like.

At block 1975, the cybersecurity AI/ML service 1742 may provide information regarding the determined remediation(s). Illustratively, the information regarding the determined remediation(s) may be presented in any of a variety of modalities, such as: text-based and/or graphic-based presentations via a GUI; text-based output provided via a chatbot; audio output via a voice-based system using pre-recorded or synthesized speech; etc. The information may identify the highest-probability remediation(s), information about implementing the remediations, etc. In some embodiments, a recommended remediation may be automatically implemented without manual intervention.

Blockchain Storage

The cybersecurity system 1720 can use blockchain technology. Blockchain can be leveraged to maintain data, such as facts, solutions, threats, or remediations, in a database. Blockchain can provide immutability benefits. Cryptographically linked blocks of data (such as facts, solutions, threats, or remediation data or documents) provide a record that can be difficult to tamper. This tamper resistance can be useful in a cybersecurity context. In some embodiments, the entire data item (such as a document) may not be stored on the blockchain (such as due to file size limitations). In these cases, an address, pointer, or other reference to the data item, and a hash of the data item for verification of the data item located at the referenced location, can be stored on the blockchain.

An illustrative blockchain algorithm is provided. The first block in the blockchain can be referred to as the genesis block. Each block can contain one or more data items or references to data items. For example, once a cyber threat has been verified and either a remediation is identified or knowledge article is authored, the information can be added to a block in the blockchain. Each block in the chain can have a record of the hash of the previous block, which can provide a degree or tamper resistance. Once a block has been validated and added to the chain, it may not be removed.

Provisioning and Configuration of Cybersecurity Assessment System

FIG. 20 is a flow diagram of an illustrative process 2000 for provisioning and configuring the cybersecurity assessment system—and services provided thereby—for a target network. Advantageously, the provisioning and configuration of an instance of the cybersecurity assessment system to provide services to a particular target network may be automatically performed based on information provided by a user (e.g., an administrator of the target network), or otherwise at the direction of the user. For example, the cybersecurity assessment system may collect metadata from a user regarding the network connections and use cases desired for one or more services. Once the metadata has been collected, the cybersecurity assessment system automatically provisions the selected services based on the provided data, such as duration of time elected, service metrics, and the like. In some embodiments, validation or identify verification procedures may be performed before or during the process 2000 to verify that the entity managing the target network is not a malicious or bad-faith actor or to otherwise validate and authorize the request for cybersecurity assessment services. When such validation procedures are performed, portions of the process 2000 may be delayed until the validation is successfully completed. For example, the instance of the cybersecurity assessment system may not be deployed and/or configured, secure connection information may not be provided, or a secure connection between the target network and the instance of the cybersecurity assessment system may not be established.

Process 2000 begins at block 2002. Process 2000 may begin in response to an event, such as when a user accesses a user interface to obtain the services of the cybersecurity assessment system for a target network. When process 2000 is initiated, a set of executable program instructions stored on one or more non-transitory computer-readable (e.g., random access memory or “RAM”) of a computing device. For example, provisioning management instructions 2582 shown in FIG. 25 may be loaded into memory 2566 of a cybersecurity assessment system computing device 2550 and executed by one or more processors 2560. In some embodiments, process 2000 or portions thereof may be implemented on multiple processors (on the same or separate computing devices), serially or in parallel.

At block 2004, a provisioning management unit or some other component of the cybersecurity assessment system 120 may receive cybersecurity assessment service setup data regarding a target network. Cybersecurity assessment service setup data may include data representing one or more services of the cybersecurity assessment system 120 to be provided for the target network. For example, the selectable services may include cybersecurity analysis, vulnerability detection, cybersecurity event detection, and the like. Cybersecurity assessment service setup data may also include data regarding the target network, for use in configuring the selected services for the target network. For example, the configuration data may include the IP address of the VPN gateway through which the cybersecurity assessment system 120 will access the target network, data identifying the individual networks, subnets, or devices to be scanned, and the like.

FIG. 21 is a diagram of an illustrative user interface 2100 configured to receive cybersecurity setup data regarding the target network. The user interface 2100 may include a target network information portion 2102, a service selection portion 2104, and a service configuration portion 2106.

The target network information portion 2102 may include fields for a user, such as an administrator of the target network, to provide information regarding the target network for which an instance of the cybersecurity assessment system is to be provisioned. The information regarding the target network may include identifying information, such as a name or other unique identifier of the target network. The information regarding the target network may also include address information, such as an IP address of the VPN through which the instance of the cybersecurity assessment system is to access the target network. The information regarding the target network may also identify the portions of the target network to be scanned by the instance of the cybersecurity assessment system.

The service selection portion 2104 may include a listing of services offered by the cybersecurity assessment system. The user may select individual services or groups thereof. For example, the user may select cybersecurity analysis in which the cybersecurity posture of the target network is evaluated with respect to one or more cybersecurity assessment frameworks. As another example, the user may select vulnerability detection in which the target network is scanned to detect specific cybersecurity vulnerabilities. As another example, the user may select cybersecurity event monitoring in which the target network is monitored for the occurrence of cybersecurity events, and alerts are generated for particular events based on use cases, rules, or the like.

The service configuration portion 2106 may include fields and/or controls for providing information to be used in providing the selected services. The user may provide information regarding use cases, desired metrics, and the like. For example, if the user has selected the cybersecurity analysis service in the service selection portion 2104, the user may select the cybersecurity assessment framework(s) against which the target network is to be evaluated, the frequency of the evaluation (e.g., one time, monthly, quarterly, annually, etc.), the duration over which the service is to be provided (e.g., one time, one month, one quarter, one year, ongoing, etc.), and the like. As another example, if the user has selected the vulnerability detection service in the service selection portion 2104, the user may select the subnetworks to be scanned for vulnerabilities, the frequency of the scanning (e.g., one time, daily, weekly, monthly, etc.), the duration over which the service is to be provided (e.g., one time, one month, one quarter, one year, ongoing, etc.), and the like. As another example, if the user has selected the cybersecurity event monitoring service in the service selection portion 2104, the user may select the devices or other event sources to be monitored, the time periods for monitoring and/or alerting (e.g., 8:00 AM-5:00 PM on business days, 24 hours daily year round, etc.), the duration over which the service is to be provided (e.g., one month, one quarter, one year, ongoing, etc.), specific use cases and rules for determining whether and how to generate alerts for particular events, and the like.

In some embodiments, the user may participate in an automated dialog with the cybersecurity system 120, and may be prompted for cybersecurity setup data. The automated dialog may be in text form (e.g., using a text-based “chatbot”) or spoken form (e.g., using pre-recorded or synthesized speech for prompts). The cybersecurity assessment system 120 may prompt the user for—or otherwise facilitate provision of—any of the cybersecurity setup data described herein with respect to graphical user interfaces. The cybersecurity assessment system 120 may process the user's responses using natural language processing (“NLP”) to identify the provided cybersecurity setup data. Such automated dialogs may expedite the setup process of provisioning selected services.

In some embodiments, cybersecurity setup data may be determined automatically instead of—or in addition to—being provided by a user through a user interface. For example, the cybersecurity assessment system may be provided with access to the target network via a secure channel, such as a VPN tunnel. The cybersecurity assessment system may scan the target network to determine devices and other entities that are to be scanned, monitored, and the like in providing the services of the cybersecurity assessment system.

FIG. 22 is a diagram of an illustrative user interface 2200 configured to present a provisioning status for the instance of the cybersecurity assessment system 120. After the user provides cybersecurity setup data regarding the target network, the cybersecurity assessment system 120 may begin provisioning an instance for the target network. The user interface unit 160 may generate user interface 2200 to present the status of provisioning to the user. As shown, the interface 2200 incudes a status summary portion 2202, a task list portion 2204, and a secure connection presentation portion 2206. The status summary portion 2202 may provide a graphical indication of the overall status of instance provisioning. As individual tasks are completed, as described in greater detail below, the status summary portion 2202 may be updated to indicate such completion. For example, the status summary portion 2202 may be a graphical status bar with a visual aspect (e.g., color, texture, etc.) that changes to indicate where the current status of provisioning is within a range of completeness. The task list portion 2204 may present a listing of individual tasks or groups of tasks to be completed when provisioning the instance. The tasks may include provisioning of the individual services selected by the user, tasks common to all services, finalization of provisioning, and establishment of a secure network connection between the target network and the instance of the cybersecurity assessment system. As individual tasks or groups are completed, a visual aspect (e.g., color, texture, checkmark, etc.) associated with the task/group may be updated to indicate completion. The secure connection presentation portion 2206 may present information to be used in establishing a secure connection with the instance of the cybersecurity assessment system 120, such as a shared secure key as described in greater detail below. The secure connection information may be presented automatically once a particular point in provisioning has been reached, such as completion of all tasks, or completion of one or more prerequisite tasks. In some embodiments, fewer, additional, or alternative portions of the user interface 2200 may be included. For example, the secure connection presentation portion 2206 may not be included. Instead, the secure connection information may be provided in another manner, the user may provide secure connection information to the cybersecurity assessment system 120, or a secure connection between the target network and the instance may be established automatically.

Returning to FIG. 20 , at block 2006, the provisioning management unit or some other component of the cybersecurity assessment system 120 may begin provisioning the instance of the cybersecurity assessment system for the target network. As shown in FIG. 23 , the provisioning management unit 2302 may create a new cybersecurity assessment system instance 2304 after receiving cybersecurity setup data for the target network 100. Creating the instance 2304 may include installing one or more virtual machines and/or other software on one or more computing devices of the cybersecurity assessment system 120. Installing the virtual machines and/or other software may be performed using one or more scripts to perform the functions of creating the instance, copying images of the virtual machines and/or other software, creating one or more databases from database design patterns, and the like. The instance 2304 may include various executable objects 2340 and data stores 2342 to provide the overall functionality of the cybersecurity assessment system 120. For example, the executable objects 2340 may include applications and/or virtual machines for providing features of the user interface unit 160, and the data stores 2342 may include one or more databases to store data regarding the target network.

At block 2008, the provisioning management unit or some other component of the cybersecurity assessment system 120 may configure the cybersecurity assessment system instance for the current target network. The instance may be configured using information obtained via a user interface (e.g., the user interface 2100 described in greater detail above), by scanning the target network, or some combination thereof. For example, the provisioning management unit 2302 may store information regarding the target network (e.g., target network identifier, VPN address, selected services) into a data store 2342 of the instance 2304. The provisioning management unit may configure a host name, load security certificates, and perform various other operations to communicate with the target network in a secure manner.

At block 2010, the provisioning management unit or some other component of the cybersecurity assessment system 120 may determine which services have been selected by the user or are otherwise to be provisioned and configured in the instance for the target network. Depending upon which services are to be provisioned, the provisioning management unit can determine which scripts are to be executed to deploy and configure the services for use with the target network. For example, if the cybersecurity analysis service is to be provisioned, the process 2000 may proceed to block 2012. As another example, if the vulnerability detection service is to be provisioned, the process 2000 may proceed to block 2016. As another example, if the cybersecurity event monitoring service is to be provisioned, the process 2000 may proceed to block 2020. If multiple services are to be provisioned, the provisioning management unit may determine an order in which to provision the services serially, or some or all of the selected services may be provisioned in parallel.

At block 2012, the provisioning management unit or some other component of the cybersecurity assessment system 120 may deploy components for providing the cybersecurity analysis service. Deploying components for providing the cybersecurity analysis service may include executing one or more scripts to deploy images of virtual machines and/or other software, creating one or more databases from database design patterns, and the like. As shown in FIG. 23 , the provisioning management unit 2302 may execute scripts to deploy various executable objects 2340 and data stores 2342 to provide the functionality of the cybersecurity analysis service. The executable objects 2340 may include applications and/or virtual machines for providing features of one or more data stream units 125A-125C, an aggregation unit 130, and a cybersecurity unit 140, described in greater detail above. The data stores 2342 may include object data stores to store scan data generated by one or more data stream units 125A-125C, and databases used by the aggregation unit 130 and cybersecurity unit 140.

At block 2014, the provisioning management unit or some other component of the cybersecurity assessment system 120 may configure the instance 2304 to provide the cybersecurity analysis service for the current target network 100 based on the configuration information provided via a user interface or otherwise obtained by the cybersecurity assessment system 120. For example, the provisioning management unit may execute API calls to certain objects 2340 and data stores 2343 to configure the objects to scan particular subnets, devices, or other entities of the target network 100, to analyze the scan data with respect to one or more cybersecurity assessment frameworks selected via the user interface 2100, and to perform the analysis on the schedule and for the duration selected via the user interface 2100.

At block 2016, the provisioning management unit or some other component of the cybersecurity assessment system 120 may deploy components for providing the vulnerability detection service. Deploying components for providing the vulnerability detection service may include executing one or more scripts to deploy images of virtual machines and/or other software, creating one or more databases from database design patterns, and the like. As shown in FIG. 23 , the provisioning management unit 2302 may execute scripts to deploy various executable objects 2340 and data stores 2342 to provide the functionality of the vulnerability detection service. The executable objects 2340 may include applications and/or virtual machines for providing features of data stream unit 125C and a transform unit 150, described in greater detail above. The data stores 2342 may include object data stores to store scan data generated by the data stream unit 125C, and databases used by the transform unit 150.

At block 2018, the provisioning management unit or some other component of the cybersecurity assessment system 120 may configure the instance 2304 to provide the vulnerability detection service for the current target network 100 based on the configuration information provided via a user interface or otherwise obtained by the cybersecurity assessment system 120. For example, the provisioning management unit may execute API calls to certain objects 2340 and data stores 2343 to configure the objects to scan particular subnets, devices, or other entities of the target network 100, to detect certain vulnerabilities at certain levels of severity, and to perform the scan on the schedule and for the duration selected via the user interface 2100.

At block 2020, the provisioning management unit or some other component of the cybersecurity assessment system 120 may deploy components for providing the cybersecurity event monitoring service. Deploying components for providing the cybersecurity event monitoring service may include executing one or more scripts to deploy images of virtual machines and/or other software, creating one or more databases from database design patterns, and the like. As shown in FIG. 23 , the provisioning management unit 2302 may execute scripts to deploy various executable objects 2340 and data stores 2342 to provide the functionality of the cybersecurity event monitoring service. The executable objects 2340 may include applications and/or virtual machines for providing features of data stream unit 125B and for applying use cases and rules for alerting particular events, described in greater detail above. The data stores 2342 may include object data stores to store scan data generated by the data stream unit 125B.

At block 2022, the provisioning management unit or some other component of the cybersecurity assessment system 120 may configure the instance 2304 to provide the cybersecurity event monitoring service for the current target network 100 based on the configuration information provided via a user interface or otherwise obtained by the cybersecurity assessment system 120. For example, the provisioning management unit may execute API calls to certain objects 2340 and data stores 2343 to configure the objects to receive and process particular event logs of the target network 100, to monitor events at certain levels of severity, and to perform the monitoring and providing alerting based on the use cases/rules, on the schedule, and for the duration selected via the user interface 2100.

At block 2024, the provisioning management unit or some other component of the cybersecurity assessment system 120 may generate one or more scripts or other executable objects to configure the target network to operate with the instance 2304 of the cybersecurity assessment system 120. Illustratively, it may be desirable to obtain event data from any devices that generate event logs and/or network flow. The instance 2304 may not be able to access certain data that is needed or desirable to provide one or more of the selected services. For example, in some cases the cybersecurity event monitoring service may not receive event data by accessing devices of the target network 100, even if accessed through a secure connection. Rather, individual devices or other entities of the target network 100 may be required to initiate transmission of—or permit access to—event data such as event logs. In order to facilitate such transmission or access, the provisioning management unit 2302 may generate one or more scripts or other executable components to provide such access. For example, devices such as user computing devices, server computing devices, routers, switches, firewalls, and other networked equipment running certain operating systems or otherwise configured in certain ways may not permit access to event logs without first being configured to do so using an administrator account. An executable object may be generated so that when it is run on the device using administrator permission, the proper access to the event log (or transmission of event log data) may be established. Different executable objects may be generated for different devices (desktops, servers, mobile devices, routers, switches, firewalls, and other networked equipment, etc.), operating systems (e.g., one for Windows® based operating systems, one for Linux based operating systems, etc.), or combinations thereof. Additional and/or alternative executable objects may be generated to provide necessary access to data on the target network 100, depending upon the particular configuration of the target network 100 as determined from information provided by a user (e.g., through interface 2100), from a scan of the target network 100, etc.

At block 2026, the provisioning management unit or some other component of the cybersecurity assessment system 120 may provide the one or more scripts or other executable components generated above to the target network. One or more devices of the target network may execute relevant executable components to configure the target network to operate with the instance of the cybersecurity assessment system. In some embodiments, a user (e.g., a system administrator) may execute the executable components using administrator access privileges so that the proper configuration can be set and the instance 2304 can obtain the data needed to provide the selected services. In some embodiments, the instance 2304 may execute the executable components on one or more devices of the target network 100 without requiring a user of the target network to initiate the execution.

At block 2028, the provisioning management unit or some other component of the cybersecurity assessment system 120 may provide secure connection information for establishing a secure connection between the target network and the instance of the cybersecurity assessment system. The secure connection information may be or include a secure key for establishing a VPN connection. As shown in FIG. 24 , the provisioning management unit 2302 may provide the secure connection information to the target network. For example, the secure connection information may be presented in user interface 2200, as described above. As another example, the secure connection information may be provided with the executable objects generated above, such as in a script to configure the firewall of the target network to provide the VPN connection endpoint. Once the endpoints on each side of the VPN connection (e.g., a firewall of the target network 100 and a firewall of the cybersecurity assessment system 120 through which the instance 2304 is accessed) are configured, the VPN connection may be established at block 2330.

At block 2030, the instance 2304 of the cybersecurity assessment system 120 may establish a secure connection with the target network. As described above, the secure connection may be a VPN connection between two secure endpoints, such as firewalls on either side of the connection. Each endpoint provides the same secure connection information (e.g., shared key) and attempts to establish the connection for accessing/permitting access to the same subnets, IP addresses, etc. If the secure connection information and access information match, then the secure connection may be established.

At block 2032, the instance of the cybersecurity assessment system 120 may begin providing the selected cybersecurity assessment services to the target network 100. For example, depending on whether cybersecurity evaluation, vulnerability detection, and/or cybersecurity event monitoring have been selected, the instance 2304 may initiate scans and processing for the selected services as described in greater detail above.

At block 2034 the process 2000 may terminate.

Example Device Components

FIG. 25 shows components of an illustrative target network device 2500 (e.g., a mobile device 102, desktop device 104, server device 106, or network hardware device such as a firewall, switch, router, etc.), and a cybersecurity assessment system computing device 2550.

In some embodiments, as shown, the target network device 2500 may include: one or more computer processors 2510, such as physical central processing units (“CPUs”); one or more network interfaces 2512, such as a network interface cards (“NICs”); one or more computer readable medium drives 2514, such as a high density disk (“HDDs”), solid state drives (“SDDs”), flash drives, and/or other persistent non-transitory computer-readable media; and one or more computer readable memories 2516, such as random access memory (“RAM”) and/or other volatile non-transitory computer-readable media. The computer readable memory 2516 may include computer program instructions that the computer processor 2500 executes in order to implement one or more embodiments. For example, the computer readable memory 2516 can store an operating system 2520 that provides computer program instructions for use by the computer processor 2510 in the general administration and operation of the target network device 2500. The computer readable memory 2516 may also include application instructions 2522, 2524 for various applications executed by the target network device 2500.

In some embodiments, as shown, the cybersecurity assessment system computing device 2550 may include: one or more computer processors 2560, one or more network interfaces 2562, one or more computer readable medium drives 2564, and one or more computer readable memories 2566. The computer readable memory 2566 may include computer program instructions that the computer processor 2560 executes in order to implement one or more embodiments. For example, the computer readable memory 2566 can store an operating system 2570 that provides computer program instructions for use by the computer processor 2560 in the general administration and operation of the cybersecurity assessment system 120. The computer readable memory 2566 may also include aggregation instructions 2572 for implementing the aggregation unit 130. The computer readable memory 2560 may also include cybersecurity analysis instructions 2574 for implementing the cybersecurity unit 140. The computer readable memory 2560 may also include transform instructions 2576 for implementing the transform unit 150. The computer readable memory 2560 may also include user interface instructions 2578 for implementing the user interface unit 160. The computer readable memory 2560 may also include access control instructions 2580 for implementing the user interface unit 160. The computer readable memory 2560 may also include provisioning management instructions 2582 for implementing the provisioning management unit.

Additional Embodiments

Features disclosed herein that include obtaining user input and/or providing output to users are not limited to the specific examples described and shown in the figures. In some embodiments, other methods of input, output, and interactivity may be used. Illustratively, the cybersecurity assessment system 120 may provide automatic natural language dialogs instead of—or in addition to—any of the graphical user interfaces and other user-facing input/output methods described herein. The automated dialogs may be in text form (e.g., using a text-based “chatbot”) or spoken form (e.g., using pre-recorded or synthesized speech) for prompts, system outputs, etc. The cybersecurity assessment system 120 may process user responses and other system inputs using NLP methods, such as automatic speech recognition (“ASR”) for voice dialogs, natural language understanding (“NLU”) for text dialogs or voice converted to text, etc.

The automated natural language dialogs may leverage data regarding assessments, solutions, threats, remediations, recommendations, and the like to guide users. For example, automated natural language dialogs may be used instead of—or in addition to—other user interfaces for performing audits of cybersecurity framework compliance, obtaining results of automated threat analysis generated using machine learning models, obtaining remediation recommendations generated using machine learning models, provisioning and maintaining the ongoing operation of cybersecurity services, and the like.

Terminology

Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described operations or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, operations or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of electronic hardware and computer software. To clearly illustrate this interchangeability, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware, or as software that runs on hardware, depends upon the particular application and design constraints imposed on the overall system. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.

Moreover, the various illustrative logical blocks and modules described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a computing processor device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A computer processor device can be a microprocessor, but in the alternative, the processor device can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor device can include electrical circuitry configured to process computer-executable instructions. In another embodiment, a processor device includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. A processor device can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor device may also include primarily analog components. For example, some or all of the algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few.

The elements of a method, process, routine, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor device, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of a non-transitory computer-readable storage medium. An exemplary storage medium can be coupled to the processor device such that the processor device can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor device. The processor device and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor device and the storage medium can reside as discrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without other input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.

While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As can be recognized, certain embodiments described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. The scope of certain embodiments disclosed herein is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. 

What is claimed is:
 1. A system comprising computer-readable memory and one or more hardware processors, wherein the system is configured to at least: obtain threat data representing a plurality of cybersecurity threats, wherein a first portion of the threat data represents a first cybersecurity threat of the plurality of cybersecurity threats, the first cybersecurity threat detected on a first target network; obtain vulnerability data representing cybersecurity vulnerabilities associated with individual cybersecurity threats of the plurality of cybersecurity threats, wherein a first portion of the vulnerability data represents a first vulnerability of the first target network at a time associated with the first cybersecurity threat; generate training data using the threat data and the vulnerability data, wherein a first portion of the training data represents the first cybersecurity threat and the first vulnerability; train a cybersecurity machine learning model using the training data, wherein the cybersecurity machine learning model is configured to generate output data representing a probability of occurrence of one or more cybersecurity threats based on a presence of one or more cybersecurity vulnerabilities; receive a cybersecurity threat analysis request associated with a second target network; obtain second vulnerability data representing cybersecurity vulnerabilities associated with the second target network; determine, based at least partly on applying the cybersecurity machine learning model to the second vulnerability data, that the second target network is associated with the first cybersecurity threat; and generate a user interface configured to present information regarding the determination that the second target network is associated with the first cybersecurity threat.
 2. The system of claim 1, further configured to at least determine, based at least partly on applying the cybersecurity machine learning model to the second vulnerability data, that the second target network is associated with a second cybersecurity threat, wherein a probability of occurrence of the first cybersecurity threat is higher than a probability of occurrence of the second cybersecurity threat.
 3. The system of claim 2, further configured to at least determine to exclude information regarding the second cybersecurity threat from the user interface based at least partly on the probability of occurrence of the first cybersecurity threat being higher than the probability of occurrence of the second cybersecurity threat.
 4. The system of claim 2, further configured to at least determine to include information regarding the second cybersecurity threat in the user interface based at least partly on the second target network being associated with the second cybersecurity threat.
 5. The system of claim 1, wherein the cybersecurity machine learning model comprises a multinomial naïve Bayesian classifier.
 6. The system of claim 1, wherein the first portion of the vulnerability data representing the first vulnerability of the first target network at the time associated with the first cybersecurity threat comprises the first portion of the vulnerability data being associated with a most-recently-completed vulnerability scan prior to detection of the first cybersecurity threat.
 7. The system of claim 1, wherein to generate the training data, the system is configured to generate an array comprising first data representing the first cybersecurity threat and second data representing the first vulnerability.
 8. A system comprising computer-readable memory and one or more hardware processors, wherein the system is configured to at least: obtain threat data representing a plurality of cybersecurity threats, wherein a first portion of the threat data represents: a first cybersecurity threat of the plurality of cybersecurity threats; a first network property of a first target network on which the first cybersecurity threat is detected; and a first remediation associated with the first cybersecurity threat; generate remediation data representing a plurality of threat remediations, wherein a first portion of the remediation data represents an association of the first remediation with the first cybersecurity threat and a second cybersecurity threat, and wherein the remediation data is generated based at least partly on a similarity between the first portion of the threat data and a second portion of the threat data representing the second cybersecurity threat; generate training data using the threat data and the remediation data, wherein a first portion of the training data represents the first cybersecurity threat, the first network property, and the first remediation; train a cybersecurity machine learning model using the training data, wherein the cybersecurity machine learning model is configured to generate output data representing a remediation based at least partly on a cybersecurity threat and a network property; receive a cybersecurity threat analysis request associated with a second target network; obtain, from the cybersecurity threat analysis request, second threat data representing a detected threat and a detected network property of the second target network; determine, based at least partly on applying the cybersecurity machine learning model to the second threat data, an association of the second threat data and the first remediation; and generate a user interface configured to present information regarding the first remediation.
 9. The system of claim 8, wherein the first remediation being associated with the first cybersecurity threat comprises the first remediation being implemented in response to detection of the first cybersecurity threat on the first target network.
 10. The system of claim 8, wherein the first network property of the first target network comprises one of: a source IP address associated with the cybersecurity threat, or a destination IP address associated with the cybersecurity threat.
 11. The system of claim 8, further configured to at least determine, based at least partly on applying the cybersecurity machine learning model to the second threat data, that the second threat data is associated with a second remediation, wherein a probability of the first remediation being predicted for the second threat data is higher than a probability of the second remediation being predicted for the second threat data.
 12. The system of claim 11, further configured to at least determine to exclude information regarding the second remediation from the user interface based at least partly on the probability of the first remediation being predicted for the second threat data being higher than the probability of the second remediation being predicted for the second cybersecurity threat.
 13. The system of claim 11, further configured to at least determine to include information regarding the second remediation in the user interface based at least partly on the second threat data being associated with the second remediation.
 14. The system of claim 8, wherein the cybersecurity machine learning model comprises a Gaussian naïve Bayesian classifier.
 15. A computer-implemented method comprising: obtaining threat data representing a plurality of cybersecurity threats, wherein a first portion of the threat data represents a first cybersecurity threat of the plurality of cybersecurity threats, the first cybersecurity threat detected on a first target network; obtaining vulnerability data representing cybersecurity vulnerabilities associated with individual cybersecurity threats of the plurality of cybersecurity threats, wherein a first portion of the vulnerability data represents a first vulnerability of the first target network at a time associated with the first cybersecurity threat; generating training data using the threat data and the vulnerability data, wherein a first portion of the training data represents the first cybersecurity threat and the first vulnerability; training a cybersecurity machine learning model using the training data, wherein the cybersecurity machine learning model is configured to generate output data representing a probability of occurrence of one or more cybersecurity threats based on a presence of one or more cybersecurity vulnerabilities; receiving a cybersecurity threat analysis request associated with a second target network; obtaining second vulnerability data representing cybersecurity vulnerabilities associated with the second target network; determining, based at least partly on applying the cybersecurity machine learning model to the second vulnerability data, that the second target network is associated with the first cybersecurity threat; and generating a user interface configured to present information regarding the determination that the second target network is associated with the first cybersecurity threat.
 16. The computer-implemented method of claim 15, further comprising: determining, based at least partly on applying the cybersecurity machine learning model to the second vulnerability data, that the second target network is associated with a second cybersecurity threat, wherein a probability of occurrence of the first cybersecurity threat is higher than a probability of occurrence of the second cybersecurity threat; and determining to exclude information regarding the second cybersecurity threat from the user interface based at least partly on the probability of occurrence of the first cybersecurity threat being higher than the probability of occurrence of the second cybersecurity threat.
 17. The computer-implemented method of claim 15, further comprising: determining, based at least partly on applying the cybersecurity machine learning model to the second vulnerability data, that the second target network is associated with a second cybersecurity threat, wherein a probability of occurrence of the first cybersecurity threat is higher than a probability of occurrence of the second cybersecurity threat; and determining to include information regarding the second cybersecurity threat in the user interface based at least partly on the second target network being associated with the second cybersecurity threat. 